Genetic dissection for seedling root-related traits using multiple-methods in bread wheat (Triticum aestivum L.)

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Naicui Wei, Yuqiong Hao, Jinbo Tao, Jiajia Zhao, Bangbang Wu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4142603/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 4 You are reading this latest preprint version Abstract The root system of wheat affects water and fertilizer use efficiency, stress tolerance, and agronomic traits. Using association analysis and linkage mapping, QTL associated with 12 seedling-stage root traits were identified with SNPs and SCVs under both hydroponic nutrient solution culture experiment (NCE) and soil culture experiment (SCE). Except for MRL, the root traits of seedlings under NCE and SCE differed significantly. Several seedling indicators, including RFW, RDW and RS, were significantly correlated with adult plant agronomic traits. Identification of RFW, RDW, RS, and RV by NCE is equivalent to SCE for subsequent research. Under NCE, 29 stable loci and 9 SCVs of 12 root traits were detected respectively by SNPs and SCVs association analysis. Under SCE, association analysis detected 23 stable loci with SNPs and 26 loci with SCVs. In the DH population, 21 stable QTL were detected by SNPs linkage analysis, and 6 SCVs were found by SCVs analysis. Co-localization analysis revealed that NCE and SCE simultaneously detected QRdw.sxau-6A, QRd.sxau-1B.2, and QDw.sxau-6A (5.56%-8.76% of R2). Mr1B-3, Mr3A-3 and Mr3A-4 were detected in both NCE and SCE (4.74%-9.07% of R2). In the association panel, SNPs and SCVs co-localized to 14 MTAs, of which Mr5A-6 and QRd.sxau-5A were significantly associated with RD. The association panel and DH population co-located 10 QTL, of which QDw.sxau-1D was stably detected. QDw.sxau-6A and Mg6A-9 overlapped in same genomic location containing candidate genes TraesCS6A02G372300, TraesCS6A02G382900 and TraesCS6A02G365100. The present study contributes novel insights into the genetics of root architecture in wheat. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Key Message Several QTL and SCVs for seeding root traits were identified using multiple methods, and these can be used to breed for improved root systems in wheat. 1. Introduction Wheat ( Triticum aestivum L.) is a major crop worldwide, accounting for about 35% of the staple food (Han et al. 2019). Roots absorb water and nutrients and directly affect growth and yield of the aboveground plant parts (Atkinson et al. 2015). Various root architecture features are associated with different functions. For example, maximum root length affects acquisition of water (Wasson et al.2012), and root number, diameter and surface area affect nutrients uptake (Colombi and Walter 2017; Meister et al.2014; Courtois et al. 2009). Root biomasses are essential for the balance between aboveground biomass and root biomass, which in turn affects yield (Bettembourg et al. 2017; Cormier et al. 2016). Root systems respond to different growing environments by changing their morphology and distribution in the soil, which in turn alter water and fertilizer use efficiency, lodging resistance and heat tolerance in wheat (Li et al. 2022; Uga et al. 2013). Thus, understanding the genetic basis of root architecture could enhance wheat breeding for many important traits. Because measuring root-related phenotypes is difficult, there are rarely and lagging genetic studies of root architecture. Commonly used methods for investigating root traits include seedling stage measurements from plants grown under controlled conditions and field evaluations at the adult stage (Atkinson et al. 2019). The methods to study roots at the adult stage include excavation, basket, and use of a minirhizotron. These approaches have great damage to root system, high labor intensity and slow speed of measurement, which is not suitable for genetic analysis of large mapping populations (Vamerali et al. 2012; Zheng et al. 2019). However, seedling-stage measurement is fast, simple, and highly correlated with root morphology and distribution at the adult stage. Consequently, analysis of roots at the seedling stage has been widely used in genetic studies of root development in wheat. Controlled environment root phenotyping methods include hydroponics (Ayalew et al.2015), clear pots (Richard et al.2015), and pots filled with soil (Cao et al. 2014). Ma et al (2022) found significant correlation of root traits between outdoor pot culture and indoor pot culture. However, root traits, such as total root length, root diameter and root tip number were more variable in outdoor pot culture. Wang et al (2023a) found significant differences in root traits between pure water hydroponic versus Hoagland nutrient solution hydroponic culture. With Hoagland nutrient solution, root length was short and root diameter was greater. Under pure water culture, the main root was longer, and the number of lateral roots was higher. However, it is unclear if there are differences in root traits between hydroponics and soil culture at the seedling. Linkage mapping and association analysis are the main approaches for quantitative trait loci (QTL) localization. In the seedling stage, QTL for root-related traits have been localized by linkage analysis on all wheat chromosomes. With RIL and DH population, a total of 51 related QTL were detected that accounted for 1.54% to 31.99% of the phenotypic variation in root traits at the seedling stage (Kabir et al. 2015). Also using a RIL population, thirteen QTL on chromosomes 1B, 2D, and 5A accounted for 9.00% to 17.00% of the phenotypic variation (Ayalew et al. 2017). Yang et al. (2021a) identified 51 QTL for root architecture and biomass-related traits at the seedling stage with a DH population, explaining 4.66%-19.12% of the phenotypic variation. Linkage analysis rapidly detects loci, genetic positions, and effects of target traits. However, linkage analysis only detects relatively favorable allelic variations in both parents. Association analysis can reduce the limitations of bi-parental QTL mapping. For example, eighteen stable QTL for five seedling-stage root traits were identified in an association panel of 189 varieties, explaining 8.68%-14.07% of phenotypic variation (Wang et al.2023a). Ma et al (2022) investigated nine root traits at the seedling stage using an association panel of 388 varieties, and a total of 36 associated QTL were identified explaining1.23%-6.77% of the phenotypic variance. A study using association analysis of a diverse population of 165 wheat cultivars identified 32 stable QTL for seedling root-related traits, which explained 8.80%-15.60% of the phenotypic variation (Liu et al. 2019). Association analysis can simultaneously detect multiple alleles at the same location. However, false positive results are common with association analysis. Therefore, the combination of association analysis and linkage analysis more accurately locates the related loci for complex traits. This method has been widely used for QTL mapping and gene identification of above-ground traits such as plant height, spike length, and thousand grain weight (Shi et al.2017; Guo et al.2023). In contrast, there are few studies combining association analysis and linkage analysis for root-related traits. In terms of gene cloning, a small number of genes for root-related traits have been reported, including TaSRL1 (Zhuang et al. 2021), TabHLH123 (Wang et al. 2023b), TaRNAC1 (Chen et al. 2018) and TaNAC69-1 (Chen et al. 2016). These studies are insufficient for exploring the complex regulatory mechanisms of the root system. In addition, the regulatory mechanisms of root traits are involved in molecular, pathway, network, and cellular levels of complexity. Aside from QTL/genes mapping, the regulatory roles of epigenetic modifications and chromosomal structural variations must be studied (Dowell et al. 2010). Chromosomal structural variations are large length sequence changes and positional changes on the genome, including presence/absence variations (PAV), copy number variations (CNV), inversions, translocations, and complex rearrangements (Gabur et al. 2019). Wheat is a heterologous hexaploid, with a large and complex genome that developed many SCVs during its evolution and selection (Dvorak et al. 2018). SCVs play an essential role in wheat growth and environmental adaptation. For example, Zhao et al. (2023) found three SCVs on chromosomes 4A, 5A, and 7B, which were significantly associated with wheat grain length, grain width, and spike length. Zhao et al. (2022) analyzed 12 agronomic traits of wheat, including 1000-grain weight, grain length, and grain width, and identified 29 significantly associated SCVs, with R 2 ranging from 1.10 % to 37.19 %. There have been some drawbacks with genetic studies of wheat root systems including: (1) uncertainty if hydroponic and soil culture conditions at the seedling stage are comparable, (2) only either linkage mapping or association analysis was used, and the major loci are rarely identified, and (3) the studies of chromosome level have not been reported. The present study used an association panel and a doubled haploid (DH) population to investigate 12 root-related traits under both hydroponic and soil culture conditions. Using SNPs and SCVs combined with association and linkage analysis, stable loci related to root traits in wheat seedlings were identified. On this basis, effects of stable loci were studied, which provided new insights into the genetics of wheat root-related traits. 2. Materials And Methods 2.1. Plant materials An association panel of 282 wheat accessions from Shanxi Province, China, was used in the present study. These genotypes include 126 irrigated and 115 dryland modern wheat cultivars. The other 41 materials were landraces from Shanxi Province selected from the Chinese wheat core collection (Zheng et al.2022). The DH population of 201 lines was generated from a cross between Jinmai 47 × Jinmai 84, and the root traits of the two parents differed significantly. 2.2. Root phenotyping of seedlings Twenty seeds of uniform size were surface sterilized by soaking in 3% H 2 O 2 for 30 min, rinsed several times with sterilized water, and placed in Petri dishes with one sheet of filter paper moistened with 5 mL of distilled water. The dishes were kept in a dark incubator at 20 °C until germination. After germination, the seeds were cultured under two conditions. In the NCE (nutrient solution culture experiment) method, five seeds with uniform germination were kept in Petri dishes for 6 days before being transferred to Hoagland nutrient solution (pH=6.0) and cultured for 14 days. The nutrient solution was continuously aerated and was replaced every 3 days (Wang et al. 2023a). The NCE was repeated two times. In the SCE (soil culture experiment) method, five uniformly germinated seeds of each variety were transferred into plastic pots (diameter, 7 cm; height, 8 cm) filled with vermiculite and 100 mL water and allowed to grow for 20 days (Zhao et al. 2021). The SCE was repeated three times. Two experiments were placed in a growth chamber with the light intensity of 500 µmol m − 2 s − 1 , a humidity of 70 %, with a 16 h/8 h light/ darkness photoperiod at 20 °C/16 °C. Twelve root-related traits were measured. Root images of three seedlings were captured using a Microtek Scanmakeri 800 plus scanner. MRL (maximum root length) was measured. RN (root number) was estimated from the images, and TRL (total root length), RS (root surface area), RV (root volume) and RD (root diameter) were measured using a Win-RHIZO system. Six biomass-related indicators were measured, including RFW (root fresh weight), SFW (shoot fresh weight), FW (plant fresh weight), RDW (root dry weight), SDW (shoot dry weight), and DW (plant dry weight). 2.3. Field experiments Six field experiments with the association panel were planted, three in the experimental station of Yaodu District, Linfen City, Shanxi Province of China (P1:36°48'N, 111°30'E) in 2020-2021 and three in the experimental station of Yanhu District, Yuncheng City, Shanxi Province of China (P2:35°20'N, 110°59'E) in 2020-2021. Each planting was considered a different environment. With the method of Qiao et al. (2022), 13 agronomic traits at the adult stage were assessed, including PH (plant height), SL(spike length), LL (flag leaf length), LW (flag leaf width), SN (spike number), BSS (base sterile spikelet number per spike), TSS (top sterile spikelet number per spike), GNS (grain number per spike), GL (grain length), GW (grain width), GT (grain thickness), TGW(1000-grain weight), and HD (heading data). 2.4. Genomic DNA extraction and genotyping Genomic DNA was extracted using the CTAB method. High-throughput genotyping was performed using a 16K SNP panel from the MOL-BREEDING Company. After eliminating markers with an allele frequency 10% missing data, or > 20% heterozygosity, 9793 high-quality SNPs were identified in the association panel for GWAS (Zheng et al. 2022). Briefly, markers with no polymorphisms between parents, severely distorted segregations, and missing rates greater than 15% were removed in the subsequent linkage analysis. Finally, 1373 SNPs were selected to construct the linkage map of the DH population. 2.5. FISH experiments and karyotype analysis Chromosome preparation and FISH used the procedure of Zhao et al (2023). Under an Olympus BX60 (Olympus Inc, Japan) microscope, the images were captured with a SPOT CCD (SPOT Cooled Color Digital, Olympus-DP80) camera. The cells with a complete number of chromosomes and good dispersion were selected for karyotype analysis with Photoshop v6.0 (Adobe Inc. USA). 2.6. Genome-wide association study of the association panel The MLM model was used for association analyses between root-related traits and markers. SNP marker-trait associations (MTAs) were tested with TASSEL 5.0 software and MTAs with a threshold of P < 0.001 were considered as significant MTAs (Zheng et al 2022). SCV MTAs were tested with TASSEL 5.0 and MTAs with a threshold of P < 0.05 were considered significant (Zhao et al.2022). MTAs repeatedly detected in two or more single environments ware considered to be stably associated loci. A Manhattan plot was generated via RStudio software to visualize the GWAS results. The extended region where the LD value between nearby SNPs and peak SNP decayed to r 2 = 0.2 was defined as the local LD based the QTL interval. 2.7. Linkage analysis of DH population QTL were detected using WinQTLCart2.5 (https://brcwebportal.cos.ncsu.edu /qtlcart/WQTLCart. htm) based the CIM method. QTL were considered significant at a threshold of LOD > 2.5. QTL repeatedly detected in two or more single environments were considered stably QTL. QTL either less than 1 cM apart or sharing common flanking markers were treated as a single locus and named according to McCouch et al. (1997). 2.8. Gene analysis of functional interval Genes within the target region were identified using the genome browser (JBrowse) the WheatOmics data website (Ma et al. 2021). The sequences of common wheat genes were retrieved based on the intervals of major QTL identified from https://urgi.versailles.inra.fr/download/iwgsc/ IWGSC_RefSeq_ Annotations/v1.0/ (Zheng et al. 2019). Functional annotation of genes in segments were done with the Gene Ontology (GO) database using the R package cluster Profiler. The RNA-seq of genes within the target region were retrieved from Wheat Expression Browser (http://www.wheat-expression.com/). 3. Results 3.1. Phenotypic variation of the association panel We analyzed the phenotypes of 12 traits in the association panel across all three environments under NCE conditions and across all four environments under SCE conditions. The frequency distributions of root-related traits were continuous and normally distributed in both NCE and SCE (Fig.S1). Differences in culture conditions affect root traits. For example, the RDW, RFW, TRL, RS, RV, and RN under SCE were higher than NCE, whereas RD was lower. Under NCE, the range of variation for RDW was the largest, from 2.43 mg to 9.10 mg. The RDW of Jinmai 72, Chang 7016 and Tai 113 were more sensitive to the change in culture conditions. Under SCE, the variation range of TRL was the largest, from 58.19cm to 156.81cm. The TRL of Jinmai 65, Jinmai 68 and Baitumai were more sensitive to the change in culture conditions, indicating varieties were greatly affected by environment. The relative variation range of RD was the smallest under the two conditions, from 0.44 cm to 0.50 cm under NCE and from 0.37 cm to 0.53 cm under SCE. The RD variation of Jinmai 16, Jinchun 15, and Jinmai 102 between NC and SC was less, indicating that these varieties were less affected by changes in culture conditions. The phenotype was analyzed according to the type of variety. Under NCE, the CV of root traits ranged from 2.29% to 19.45% for irrigated cultivars, from 2.07% to 17.54% for dryland cultivars, and from 1.73% to 18.48% for landraces. Under SCE, the CV of root traits ranged from 5.94% to 14.60% for irrigated cultivars, from 3.04% to 13.23% for dryland cultivars, and from 2.58% to 14.21% for landraces (Table.S1). In general, the root phenotype of the seedling stage was various and had a wide range of genetic variation. Among the variety types, phenotypic variation was the largest in irrigated cultivars, less in dryland cultivars, and lowest in landraces. Different culture conditions led to different phenotypes of root traits at the seedling stage. The root biomass (RDW and RFW), TRL, RS, RV, and RN were significantly lower under NCE than under SCE, while RD was significantly higher than under SCE (Table.1). Compared with SCE, the variation in root traits was more abundant under NCE. The CV of root traits at the seedling stage in NCE was between 2.25 % and 20.80 %, among which the CV of RDW was the greatest and the CV of RD was the least (Table.1). Under SCE, the CV of root traits at seedling stage was between 5.61% and 15.03 %, among which the CV of SDW was the greatest and the CV of RDW was the least (Table.1). ANOVA showed that there was no significant difference in MRL between the two culture conditions, while the differences between NCE and SCE for other traits were significant ( P <0.05). Fig.1. Phenotypes for root traits at the seedling stage in the association panel. a and b represent the root phenotype under NCE and SCE, respectively. c and d represent the root phenotypic differences of cultivars under NCE / SCE. Among them, c represents the number of lateral roots, TRL, and number of root tips of Jinmai 65 were significantly higher at SCE than NCE. d represents that no significant difference in root traits between NCE and SCE in Jinmai 102. e represents the correlation analysis between seedling traits and adult agronomic traits NCE, nutrient solution culture experiment; SCE, soil culture experiment MRL, maximum root length; RFW, root fresh weight; RS, root surface-area; RV, root volume Table.1. A Statistical analysis of 12 root-related traits at the seedling stage under NCE and SCE conditions Traits NCE SCE P Range Mean CV % Range Mean CV % SDW mg 13.41–15.98 14.31±0.39 2.75 9.33–22.57 15.50±2.33 15.03 ** RDW mg 2.43–9.10 4.51±0.94 20.80 9.85–16.83 10.93±0.61 5.61 ** DW mg 16.22–23.34 18.82±1.24 6.56 18.59–45.68 26.43±3.07 11.60 * SFW mg 142.88–172.78 153.97±4.59 2.98 71.20–170.10 110.32±15.31 13.88 ** RFW mg 50.68–71.38 58.98±4.13 7.01 76.50–152.89 113.95±13.25 11.63 ** FW mg 196.49–244.77 212.91±8.02 3.77 148.71–313.17 224.27±27.21 12.13 * MRL cm 13.65–19.59 16.29±1.07 6.58 13.72–18.36 16.20±0.94 5.81 NS TRL cm 33.43–58.78 43.77±4.43 10.12 58.19–156.81 104.10±14.95 14.36 * RS cm 2 6.90–10.07 8.28±0.53 6.40 7.53–16.46 12.21±1.55 12.69 ** RV cm 3 0.09–0.13 0.11±0.01 4.84 0.10–0.22 0.15±0.02 14.45 ** RD cm 0.44–0.5 0.46±0.01 2.25 0.37–0.53 0.43±0.03 6.69 ** RN 2.68–5.14 5.10±0.15 2.87 3.37–6.34 5.16±0.45 8.64 ** SDW, shoot dry weight; RDW, root dry weight; DW, plant dry weight; SFW, shoot fresh weight; RFW, root fresh weight; FW, plant fresh weight; MRL, maximum root length; TRL, total root length; RS, root surface-area; RV, root volume; RD, root diameter; RN, root number CV, coefficient of variation **, significant difference at P < 0.01; *, significant difference at P < 0.05 NCE, nutrient solution culture experiment; SCE, soil culture experiment 3.2. Relationship between seedling root traits and agronomic traits in the association panel Root traits at the seedling stage often reflect root morphology and distribution at the adult stage. The correlation between 12 root-related traits under SCE at the seedling stage and 13 agronomic traits at the adult stage was analyzed. RDW, RFW, RS, RV, SDW, SFW, DW, and FW were significantly correlated with more than 10 agronomic traits (Fig. S2a). Under the NCE condition at the seedling stage, RFW, RDW, RS, and RV were significantly correlated with agronomic traits, while SDW, DW, SFW, and FW were not significantly correlated with most of agronomic traits (Fig. S2b). On this basis, the correlation between the same traits under SCE and NCE conditions at the seedling stage was compared, and RFW, RDW, RS, and RV were significantly correlated under the two culture conditions (Fig. S2c). This result indicated that the method of hydroponic identification of RFW, RDW, RS, and RV at the seedling stage could replace the method of soil culture to further study the relationship with agronomic traits, while SDW, DW, SFW, and FW were more accurately assessed under soil culture conditions. To study the relationship between root traits at the seedling stage and agronomic traits at the adult stage of different types of varieties, correlation analyses were performed between root-related traits at the seedling stage and agronomic traits of irrigated cultivars and dryland cultivars under SCE conditions. There were significant correlations between root traits at the seedling stage and PH, TGW, GL, GW, and GT at the adult stage in dryland cultivars, but the correlation was lower in irrigated cultivars (Table S2). A comparison of root phenotypes of irrigated cultivars and dryland cultivars showed that the MRL of dryland cultivars at the seedling stage was slightly longer than that of irrigated cultivars (Table S1). Therefore, agronomic traits such as PH, TKW, GL, GW, and GT were compared between irrigated cultivars and dryland cultivars. The PH, TKW and GW of dryland cultivars were significantly higher than those of irrigated cultivars, which indicated that the longer root length of dryland cultivars may have promoted more biomass accumulation and yield formation under the same growing conditions. 3.3. Association analysis of association panel with SNP markers Association analysis between 12 root-related traits and SNP markers was performed using the MLM model. Under NCE conditions, a total of 29 stable loci distributed on 11 chromosomes were identified, which explained 5.08% to 23.25% of the phenotypic variation. Thirteen of these loci have been reported to be associated with root architecture (Fig.S3,Table.2). The significance level of QDw.sxau-1B.2 ranged from 9.02 to 11.90 in all three environments, with a R 2 of 16.65% to 23.25%. Furthermore, QDw.sxau-2A.1 was stably detected in all three environments, with R 2 ranging from 5.98% to 9.02%(Fig.S3,Table.2). Under SCE conditions, GWAS identified 23 loci for 12 seedling traits in different environments on 11 chromosomes, including chromosomes 1A, 1B and 2B, which explained 5.56% - 17.02% of the phenotypic variation. Five of these loci have been reported previously to be associated with root architecture. The QRdw.sxau-2D.2 had the highest phenotypic variation rate, with a R 2 of up to 17.02% (Fig.S3,Table.2). Table.2. Characteristics of significant loci related to root traits for wheat under two culture conditions. Traits Loci Name Culture system Chr. Position (Mb) -log10( P ) R 2 (%) References SDW QSdw.sxau-2A.1 NCE 2A 305.69-305.69 3.17-3.39 5.80-6.01 QSdw.sxau-2A.2 NCE 2A 747.08-747.08 3.23-3.72 5.99-6.24 Li et al. 2020 QSdw.sxau-2B SCE 2B 135.69-135.69 3.56-4.00 7.03-8.56 QSdw.sxau-3D NCE 3D 602.07-602.07 3.88-4.02 6.62-7.59 Salarpour et al. 2020 QSdw.sxau-6A NCE 6A 597.62-597.62 3.44-3.53 5.75-6.56 Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020 RDW QRdw.sxau-2A NCE 2A 191.20-194.09 3.18-3.66 5.40-6.41 QRdw.sxau-2D.1 SCE 2D 579.39-585.48 3.92-7.51 8.40-16.75 QRdw.sxau-2D.2 SCE 2D 586.95-593.12 4.11-7.62 8.82-17.02 QRdw.sxau-5D.1 SCE 5D 359.10-359.20 4.91-6.18 10.62-13.57 QRdw.sxau-6A SCE/NCE 6A 596.50-602.16 3.43-6.57 5.99-14.49 Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020 QRdw.sxau-6B NCE 6B 512.21-512.21 3.51-4.28 5.98-8.00 DW QDw.sxau-1B.1 NCE 1B 110.64-110.64 5.54-7.01 9.96-12.89 QDw.sxau-1B.2 NCE 1B 135.81-135.81 9.02-11.90 16.65-23.25 QDw.sxau-1D NCE 1D 35.79-43.72 3.72-5.02 7.24-16.34 Yang et al. 2021a QDw.sxau-2A.1 NCE 2A 305.69-305.69 3.16-5.10 5.98-9.02 QDw.sxau-2A.2 NCE 2A 327.21-327.21 3.89-5.89 6.66-10.49 QDw.sxau-2B NCE 2B 771.81-771.81 4.94-6.96 9.15-13.01 QDw.sxau-2D SCE 2D 584.52-589.19 3.56-6.50 6.94-14.34 QDw.sxau-4B NCE 4B 526.96-526.96 6.20-6.94 11.47-12.44 QDw.sxau-5D NCE 5D 450.20-450.20 5.92-6.63 10.54-11.71 QDw.sxau-6A SCE/NCE 6A 596.50-602.16 3.58-6.19 5.56-13.60 Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020 QDw.sxau-6B SCE 6B 714.44-714.44 3.02-3.66 5.94-8.08 SFW QSfw.sxau-1B NCE 1B 514.94-514.94 3.35-3.77 6.18-6.35 Huang et al. 2020 QSfw.sxau-2A NCE 2A 305.69-305.69 3.44-3.71 5.92-7.02 QSfw.sxau-2B SCE 2B 135.69-135.69 3.03-3.69 6.36-7.76 QSfw.sxau-3D NCE 3D 602.07.602.07 3.08-3.21 5.49-5.73 Salarpour et al. 2020 QSfw.sxau-5A SCE 5A 100.59-109.92 3.08-3.35 5.90-6.42 QSfw.sxau-6A NCE 6A 609.31-609.31 3.05-4.05 5.80-7.52 Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020 QSfw.sxau-7B SCE 7B 169.59-169.59 3.23-3.54 6.20-7.14 RFW QRfw.sxau-5B NCE 5B 138.30-138.30 3.07-3.19 5.04-5.93 FW QFw.sxau-2A NCE 2A 305.69-305.69 3.00-3.76 5.08-7.13 QFw.sxau-2B SCE 2B 135.69-135.69 3.13-3.43 6.66-7.37 QFw.sxau-3D NCE 3D 602.07-602.07 3.28-3.39 5.69-6.13 Salarpour et al. 2020 QFw.sxau-5A SCE 5A 382.13-382.14 3.02-4.10 5.81-8.42 QFw.sxau-6A NCE 6A 609.31-609.31 3.23-3.93 5.31-7.31 Yang et al. 2021b Ayalew et al. 2017 Li et al. 2020 MRL QMrl.sxau-1B SCE 1B 38.01-38.01 3.16-3,36 7.76-8.43 QMrl.sxau-4B NCE 4B 37.70-37.70 3.11-3.15 5.25-5.91 Zheng et al. 2019 QMrl.sxau-7A SCE 7A 709.98-709.98 3.21-5.35 8.83-11.62 Yang et al. 2021b RV QRv.sxau-2A NCE 2A 55.93-55.93 3.52-3.69 5.43-6.47 Yang et al. 2021a QRv.sxau-3B SCE 3B 318.27-318.27 3.11-3.34 6.45-7.04 RD QRd.sxau-1A SCE 1A 592.35-592.35 3.31-5.22 6.55-10.26 Li et al. 2020 Salarpour et al. 2020 Yang et al. 2021a QRd.sxau-1B.1 SCE 1B 408.45-408.45 3.10-3.51 6.42-7.35 QRd.sxau-1B.2 SCE/NCE 1B 492.38-492.75 3.01-3.29 6.04-6.16 QRd.sxau-4A NCE 4A 450.89-450.89 3.08-4.80 5.25-8.39 QRd.sxau-5A SCE 5A 502.69-502.69 3.42-3.83 7.17-7.40 QRd.sxau-6A NCE 6A 546.08-546.08 3.20-3.31 5.44-5.68 RN QRn.sxau-1A SCE 1A 367.05-367.05 3.10-3.20 6.34-6.41 QRn.sxau-5A.1 SCE 5A 394.56-394.56 3.45-3.84 7.13-7.74 Ayalew et al. 2017 QRn.sxau-5A.2 SCE 5A 684.73-684.73 3.00-3.59 6.70-7.21 SDW, shoot dry weight; RDW, root dry weight; DW, plant dry weight; SFW, shoot fresh weight; RFW, root fresh weight; FW, plant fresh weight; MRL, maximum root length; RV, root volume; RD, root diameter; RN, root number NCE, nutrient solution culture experiment; SCE, soil culture experiment 3.4. Distribution and association analysis of SCVs in the association panel ONPM # 7, a powerful tool for studying structural variation diversity and genetic effects, is composed of 12 sequences, and was used to identify PAVs/CNVs alleles at 202 SCVs loci across all 21 wheat chromosomes (Zhao et al. 2023). A total of 74 polymorphic SCVs were identified by FISH in the association panel, which were located on 17 chromosomes, including 1A, 1B and 1D (Zhao et al. 2022). In the DH population, 13 polymorphic SCVs located on 11 chromosomes were identified by FISH. In the association panel, GWAS identified 9 and 26 SCVs under NCE and SCE conditions, respectively (Table S3). These significant SCVs were distributed on 12 chromosomes, explaining 4.48% - 11.76% of the phenotypic variation. Thus, SCVs have important effects on root architecture. Under NCE conditions, three SCVs were detected in three datasets. The highest R 2 value (8.13%) was found for the association between Mg1B-1 and RFW, which was confirmed in all three environments. Mg1B-1 and Mr1B-3 had significant effects on RFW, FW, TRL, RV, and RS , with R 2 ranging from 5.74% to 9.07%. In addition, Mg2B-9 was significantly associated with DW, Mg1B-1 and Mr1B-3 were significantly associated with RS, and Mr1B-3 was significantly associated with TRL in all three environments, with R 2 ranging from 4.55% to 9.07%(Fig.3, Table S3). Under SCE conditions, twelve SCVs were detected in three or more datasets. The highest R 2 ranging from 10.86% to 11.76% were found for the association between Mr5B-11 and RDW. Mr1B-3 was significantly associated with 10 traits simultaneously, including FW, TRL, RS, RV and RN, explaining 5.77% - 9.86% of phenotypic variation. In addition, several SCVs were stably detected in three or more environments, including Mg1D-2 associated with DW, Mr1A-1 associated with MRL, Mg2B-12 associated with RFW, Mr1D-5 associated with RV, Mg1D-2, Mg2B-6, Mg6A-10, Mr6A-2 and Mg6A-9 associated with SDW, and Mr3A-3 and Mr3A-4 associated with SFW (Fig.3, Table S3). Fig.2. Identification of SCVs based on karyotype after ONPM # 7 FISH. a Blue indicates chromosomes of Chinese Spring counterstained with DAPI. b Green indicates BSCL135-1, BSCL135-2, and (GAA)10 modified with FAM. c Red indicates oligonucleotides pAs1-1, pAs1-3, pAs1-4, pAs1-6, pSc119.2–1, AFA-3, AFA-4, Grass-5S-1, and Grass-5S-2 modified with TAMRA. d Karyotype of Chinese Spring via merging of panels a, b, and c. e SCVs significantly associated with root traits. 3.5. Linkage analysis of SNPs and SCVs analysis in the DH population To comprehensively and systematically verify the results of GWAS in the association panel, QTL mapping was performed on 201 lines using the DH population. Twenty-one stable QTL were detected by linkage analysis of 12 seedling root traits (Table S4). Five QTL were found for MRL, explaining 5.11 % -11.03 % of the phenotypic variance. The phenotypic variation explanation rate of QMrl.sxau-6B.1 was the highest. QTrl.sxau-6B.1 and QTrl.sxau-6B.2 explained 11.34-12.34 % of the phenotypic variation for TRL. QRv.sxau-5A was detected for RV, explaining up to 15.01 % of the phenotypic variation. QRd.sxau-1B.3 and QRd.sxau-4D explained 8.51%–17.51% of the phenotypic variance for RD. In addition, seven QTL, including QRfw.sxau-6B.2 , QMrl.sxau-6B.1 , and QRd.sxau-4D , have been reported to be associated with root structure (Table S4). The R 2 of six QTL were higher than 10 %, including QMrl.sxau-6B.1 , QRv.sxau-5A and QRd.sxau-4D , were identified as major and stable QTL in multiple environments (Table S4). Among these QTL, QRv.sxau-5A , QTrl.sxau-6B.1 , QTrl.sxau-6B.2 and QMrl.sxau-6B.1 had additive effects on root-related traits such as maximum root length, total root length, root surface area and root biomass (Fig. S4). The SCV of polymorphic chromosomes from the DH population with positive impact on root-related traits were PAV.2A.1+2 , PAV.2A.1, PAV.2A.2, 1BL/1RS, CNV.4B, and PAV.6B. Each SCV was associated with one or more root traits during the seedling stage. PAV.2A.1+2 was significantly associated with SDW, DW, MRL, RDW, and RS. PAV.2A.1 was significantly associated with RDW, MRL, TRL, and RS. PAV.2A.2 was significantly associated with SDW, DW, MRL, and RV. 1BL/1RS was significantly associated with SDW, DW, MRL, TRL, and RD. CNV.4B was significantly associated with TRL and SDW. PAV.6B was significantly associated with SDW, SFW, FW, MRL, and TRL. Thus, SCV had a significant effect on root-related traits. 3.6. Co-location analysis of multiple methods 3.6.1 Co-localization analysis of SCE and NCE in association panel Three stably loci, QRdw.sxau-6A , QRd.sxau-1B.2 , and QDw.sxau-6A , were detected in both the SCE and NCE conditions based on SNP marker association analysis. QRdw.sxau-6A was composed of four significant MTAs, which were in the 596.50-602.16 Mb physical interval on chromosome 6A, with the R 2 values ranging from 5.99 to 14.49%. QDw.sxau-6A was also co-located in the same genetic interval with a R 2 value range of 5.56%-13.60% for four MTAs (Fig.3, Table.2). This genetic interval was also reported to be significantly correlated with root length. In addition, QRd.sxau-1B.2 was located at 492.38-492.75Mb on chromosome 1B, explaining 6.04%-6.16% of phenotypic variation, and has not been reported previously. Mr1B-3, Mr3A-3 and Mr3A-4 were identified under both NCE and SCE conditions based on SCV association analysis, explaining 4.74%–9.07% of the phenotypic variation (Table S3). Mr1B-3 was located on chromosome 1B at 69.79 Mb and had effects on RFW, RS, RV, TRL, and FW. In addition, Mr3A-3 at 566.23Mb and Mr3A-4 at 685.43Mb on chromosome 3A explained 4.74-5.38% of the phenotypic variation，which were significantly associated with TRL in four datasets. These results showed that SCVs were positively associated with root-related traits. Fig.3. Identification of for DW and RD with multiple methods. a represents the co-localization map of DW, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (A), SCE conditions (B), linkage analysis of a DH population with SNPs (C), and association analysis of SCV (D); b represents the co-localization map of RD, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (E), association analysis of SNPs under SCE conditions (F-G), and association analysis with SCV (H) DW, plant dry weight; RD, root diameter 3.6.2 Co-localization analysis of SCV and SNP in association panel A total of fourteen MTAs were co-located via SNP and SCV marker association analysis (Table S5). Under NCE conditions, eight MTAs were detected with R 2 ranging 4.64% to 7.74% for SNP-MTAs and 4.62% to 6.04% for SCV-MTAs. Under SCE conditions, a total of six MTAs were co-located via SNPs and SCVs, with R 2 ranging 6.59% to 14.49% for SNP-MTAs and 5.68% to 6.82% for SCV-MTAs. Among these MTAs, Mr5A-6 and QRd.sxau-5A ( 5A _ 502688179 ) were significantly associated with RD, explaining the highest R 2 of root traits (Fig.3, Table.S5). 6A_597623440 (Mg6A-9) had the same physical location as the peak markers of QSdw.sxau.6A, QRdw.sxau.6A , and QDw.sxau.6A (Fig.3). These loci were stable in multiple environments, indicating that 6A_597623440 was a stable locus associated with the seedling root traits, and suggesting that related genes regulating root architecture may exist within this loci interval on chromosome 6A. The locus QDw.sxau-6A was stably detected in both NCE and SCE, explaining 5.56%-13.60% of the phenotypic variation. Mg6A-9 was significantly and stably associated with DW in SCE, explaining 6.03-6.35 % of the phenotypic variation. According to the Zheng et al. (2022) analysis of LD attenuation distance, QDw.sxau-6A and Mg6A-9 were on the same locus (Fig.4a-c). In the same genetic interval, Mr6A-11 and Mg6A-10 also were significantly associated with root traits, indicating that this interval on chromosome 6A is an important region for regulating root structure. The locus for the peak SNP marker 6A_597623440 in QDw.sxau-6A had positive effects on root traits. The favored allele for this locus had significant effects on the root traits RDW, MRL, RS, TRL, RV, and RN. Varieties with favorable alleles showed increases in RDW, MRL, RS, TRL, RV, and RN (Fig.4d), indicating that QDw.sxau-6A had significant effects on root traits. Fig.4. Analysis of QDW.saw-6A . a Idiogram of Mg6A-9 identified by Oligo-FISH. b Physical map for QDW.saw-6A . c Manhattan plot of QDW.saw-6A identified by GWAS. d Effect of QDW.saw-6A on root related traits. RDW, root dry weight; MRL, maximum root length; RS, root surface-area; TRL, total root length; RV, root volume; RN, root number 3.6.3 Co-localization analysis of association panel and DH population To more precisely locate loci related to root traits, the co-localization results of the association panel and the DH population were analyzed. Among the QTL obtained by linkage analysis, 10 QTL were also identified via association analysis (Table S6). Five QTL were stably detected in multiple datasets of the DH population, explaining 4.59 % to 17.51 % of the phenotypic variation. QRd.sxau-4D was detected in all datasets of the DH population with the highest R 2 value. This locus has been reported as significantly correlated with root-shoot ratio (Yang et al. 2021a). The physical location of QMrl.sxau-3A in the DH population was 0.3 Mb from the locus Mr3A-4 related to the TRL in the association panel, which explained R 2 of 5.11 % (Table S6). In addition, QDw.sxau-1D was stably detected by multiple datasets in both the association panel and the DH population (Fig.5a-b). QDw.sxau-1D explained 5.17-5.92 % of the phenotypic variation for DW in the DH population and 7.24-16.34 % in the association panel. This locus also has been reported to be significantly related to the root diameter (Yang et al. 2021a). The favorable alleles of QDw.sxau-1D had significant effects on root traits RDW, RFW, DW and RV (Fig.5c). Fig.5. Analysis of QDW.saw-1D . a Manhattan plot of DW with markers located on chromosome 1D. b Physical map for QDW.saw-1D . c Genetic linkage map for QDW.saw-1D . d Effect of QDW.saw-1D on root-related traits. RDW, root dry weight; DW, plant dry weight; RFW, root fresh weight; RV, root volume 3.7. Effects of loci in association panel The effect of loci on root traits was analyzed in the association panel according to the type of allelic variation. For phenotypic traits and SNPs, variation at the nine loci differed in their effects on root traits (Fig.S5). Of these, 1B _ 110637082 , 2A _ 327206091 and 6A _ 597623440 had the strongest effects (Fig.S5). The effects of 1A_592348186 , 2D_584522338 , 5D_450204045 and 5D_359103858 were smaller. Among these loci, the proportions of favored alleles for loci 1A_592348186 were more than 90%, whereas the proportion for loci 5D_450204045 and 5D_359103858 were less than 10%. The association analysis between phenotypic traits and SCV showed that 11 loci varied significantly in their effects on root traits (Fig.S6). Mr1D-5, Mr3A-3, and Mr3A-4 significantly affected more than nine root traits, while Mg1D-2 had lesser effect. The proportions of favorable alleles for loci Mr1A-1 and Mr2B-12 were more than 80%, whereas Mg1D-2, Mr3A-3, and Mr3A-4 were less than 10%. Molecular marker-assisted selection requires markers with strong effects. At present, only a single type of marker such as SNP or SSR are used to study additive effects, and there is no report describing the combined effects of cytological markers and SNP markers. In the present study, the additive effects on root traits of loci were analyzed by combining SNPs and SCVs. Markers with significant effects on all root traits were selected for analysis and additive effects were analyzed based on peak markers. Four SNPs and five SCVs had additive effects on root traits, including 1B_110637082 , 2A_327206091 , 5D_359103858 , 6A_597623440 , Mr5B-11, Mr1D-5, Mr2B-12, Mg2B-6 and Mr3A-4. The average values of root traits increased as the number of positive alleles increased (Fig. 6). This result confirms the usefulness of these markers for selecting wheat cultivars for improved root systems. Fig.6. Effects of increasing numbers of favorable alleles for stable loci on root traits using SNPs and SCV in the association panel RFW, root fresh weight; RS, root surface-area; RV, root volume; TRL, total root length 3.8. Identification of candidate genes for the SNP and SCV co-localized locus QDw.sxau-6A Combined analyses of the two types of markers indicated that some of the overlapping segments were close to candidate genes. Overall, 439 high-confidence genes from the functional interval were annotated. Gene annotation and orthologous gene analysis indicate that TraesCS6A02G372300 , TraesCS6A02G382900 and TraesCS6A02G365100 are likely involved in either root development or lateral root formation. Gene annotation for TraesCS6A02G372300 indicated an association with lateral root development and maintenance of root viability, with homologous genes RID2 ( AT5G57280 ) (Konishi et al. 2003) in Arabidopsis and Os02g0804300 in rice. TraesCS6A02G382900 was associated with regulation of root development and nutrient reservoir activity, and it was homologous to Os01g0284500 in rice. The function of TraesCS6A02G365100 was annotated as related to lateral root formation and growth regulation. It was homologous to RLF ( AT5G09680 ) in Arabidopsis and Os07g0232200 in rice. RLF has been shown to control early cell division in the initiation of lateral roots formation (Ikeyama et al. 2010). The expression levels of genes within the functional interval were analyzed. There was high variation between the expression patterns in different tissues. TraesCS6A02G414300 (G35) and TraesCS6A02G399500 (G15) were specifically expressed only in roots, and TraesCS6A02G356900 (G33) was specifically expressed in embryonic and adventitious roots. TraesCS6A02G382900 (G6) and TraesCS6A02G403000 (G40) showed high expression levels in root, suggesting that both are candidate genes for QDw.sxau-6A (Fig.7). Fig.7. a The Manhattan plot for QDw.sxa u-6A; b Linkage disequilibrium heatmap of candidate region on chromosome 6A; c Transcriptome data for candidate region genes in different tissues. A, radicle; B, roots; C, axillary roots; D,shoots; E, coleoptile; F, stem; G, flag leaf; H, leaf; I, pistil; J, stamen; K, spike; L, spikelets; M, grain; N, endosperm; O, aleurone layer 4. Discussion 4.1. Soil culture condition at the seedling stage accurately reflects the root structure at the adult stage Root systems are the most important part of absorbing water and nutrients from the soil, and the differences in root architecture directly affects absorption efficiency (Ober et al. 2021). Root architecture is made up of traits with different functions, such as root length, number of roots, root surface area and root biomass. Wheat root-related traits are complex quantitative traits. Genetic improvement of wheat has mainly focused on agronomic and yield-related traits of aboveground parts (Guo et al. 2023; Bennani et al. 2022; Liu et al. 2017; Bennett et al.2012). By contrast, the knowledge of morphology and genetic regulation of root systems in wheat is lacking. However, root architecture directly affects the morphology and yield of the above-ground plant parts. Thus, strengthening the identification of root characteristics and analyzing the genetic basis of root traits will aid breeding for improved root systems. The difficulties in measurement of root-related traits in the field have hindered genetic dissection. Consequently, measuring roots at the seedling stage under controlled conditions has been used to represent the morphology and distribution of the roots at the adult stage. There are many methods for investigating root traits at the seedling stage, such as hydroponics with nutrient solution (Ayalew et al. 2017), gel chambers (Christopher et al. 2013), a paper culture (Bai et al. 2013), sand culture (Hamada et al. 2012) and soil culture (Cao et al. 2014). Seedling stage measurements are small experimental error, simpleoperation, high throughput, and low sampling damage. Combined with software for digital image analysis, the root-related traits of wheat can be measured rapidly. Xie et al. (2017) assessed root depth, root width, and TRL by using a germination paper-based ‘pouch and wick’ phenotyping system and found that more seminal roots and longer TRL at the seedling stage were crucial for early root growth and high yield at the adult stage. With a soil culture method Zhao et al (2021) identified 13 indicators, such as RS, root volume and root biomass, and found that the maximum root length and root biomass could be used for excellent germplasm selection. As verified in the present study, Liu et al (2019) found a significant correlation between seedling root traits under hydroponic and soil culture conditions. However, due to differences in experimental methods and germplasm, previous studies did not analyze the differences between hydroponic and soil cultures. In the present study, the root traits of 282 wheats were compared under both NCE and SCE and significant differences between the two culture methods were found. The root traits of seedlings grown under SCE conditions significant correlated with certain agronomic traits of adult plants grown under field conditions. Correlation coefficients between SDW, DW, SFW, FW and most agronomic traits were significant in SCE, but non-significant in NCE. These results showed that accurate phenotypes could be obtained from SCE-grown seedlings. Because SCE and NCE measurements were correlated for some traits, the seedling NCE measurements of RFW, RDW, RS and RV could be used to replace the more laborious SCE. 4.2. SCV provides novel insight for locating genes related to root traits in wheat Root-related traits are regulated at the molecular, pathway, network, and cellular levels. At present, QTL and genes related to roots have been reported at the nucleic acid level. For example, Alemu et al (2021) evaluated traits such as total root length, root number and root biomass with 192 Ethiopian durum wheats and identified 18 loci affecting the absorption of water and nutrients by the root system. Using a DH population, thirty-four QTL related to root architecture and biomass were identified, among which QRRS.caas-4DS had the highest R 2 of 20.41%, which was crucial for improving nutrient utilization efficiency of the root system (Yang et al. 2021b). The RIL population was used to evaluate five root traits such as total root length, root surface area and root volume, and a total of four root-related QTL were identified (Yang et al. 2017). Some genes related to roots have been reported. Wang et al. (2023b) found that TabHLH123 regulated the expression of genes controlling crown-root development and auxin transport. Hu et al. (2018) identified wheat TaWRKY51 as a crucial factor that functions in lateral root formation by regulating ethylene biosynthesis. Zhuang et al. (2021) cloned a novel ERF transcription factor gene TaSRL1 from wheat which is mainly expressed in root and is associated with root traits. In summary, some progress has been made in identifying genetic effects of root-related QTL and genes at the nucleic acid level. However, there are few studies on root development and regulatory mechanisms at the chromosome level. The variation of chromosome structure plays an important role in environmental adaptability, stress resistance, and the formation of desirable traits in wheat (Zhang et al. 2023; An et al. 2019). Thus, the identification of chromosomal structural variants can provide new insights for the genetic characterization of root traits. In wheat, the genetic effects of SCVs have been studied rarely, and only SCVs associated to agronomic traits have been reported (Zhao et al. 2022; Zhao et al. 2023). In the present study is the first to study SCVs related to root traits at the seedling stage. In the association panel, 14 SCVs significantly associated with root traits were detected. Mr1B-3, Mr3A-3, and Mr3A-4 were stably detected by multiple datasets under both SCE and NCE culture conditions, explaining 4.74% - 9.07% of the phenotypic variance. Among these, Mr1B-3 was significantly associated with many root traits such as root biomass, root surface area, root volume, and total root length. Compared to germplasm without favorable alleles, accessions with favorable alleles for locus Mr1B-3 CC showed greater RDW, RFW and RV (Fig.S6). Co-localization analysis showed that QDw.sxau-6A (association analysis between root traits and SNPs) and Mg6A-9 (association analysis between root traits and SCVs) were located in the same physical position on chromosome 6A. The genetic contribution of locus QDw.sxau-6A (Mg6A-9) for root traits was high (Fig.4d) and candidate genes for this locus are probably related to the regulation of root structure. Breeders always select alleles associated with superior phenotypes (Wu et al. 2022), including adaptation of root structure to the local environment. The requirements of root structure change will vary depending on specific wheat breeding goals. In the present study, the allelic variants for loci peak markers were associated with effects on root structure, such as 1B_110637082 , 2A_327206091 , Mr5B-11, and Mr2B-12. These markers have potential for practical use in different wheat breeding regions. In rainfed dryland areas, which tend to select deep-rooted varieties, Hap1-type markers can be used. For example, Mr3A-4-Hap1-A increased the maximum root length and total root length, and Mr1D-5-Hap1-A increased root biomass and root number, and Mr5B-11-Hap1-C increased root biomass and root surface area. In addition, these markers have additive effects. Therefore, combined marker Hap1 types could be used to select deep-rooted and large-rooted varieties in arid and semi-arid areas. In contrast, shallow-rooted varieties improve overall yield per unit area in regions with multiple irrigations by increased density of planting and population advantages. Markers for decreasing root mass and size (Hap2-type) could be used for selection in areas with multiple irrigation, including markers for Mr3A-4-Hap2-C, Mr1D-5-Hap2-C and Mr5B-11-Hap2-G. As combinations of these alleles usually show additive effects for root improvement, selection using multiple markers will be more effective than selection using single markers. Hence, pyramiding markers for favorable allelic should benefit future breeding for root improvement. The present study provides different types of molecular markers for root improvement and provide a new direction for the integration and use of multiple markers. 4.3. Association analysis combined with linkage analysis to identify loci Seedling root traits are inherited together, and it is difficult to independently select for individual traits. Traditional analysis of complex traits usually uses linkage analysis in biparental populations to estimate the effect of the target trait (Zandipour et al. 2020). However, linkage analysis only detected relatively favorable allelic variations in both parents. GWAS based on linkage disequilibrium does not construct large mapping populations to detect multiple alleles at the same position. In recent years, combining association analysis with linkage mapping has been used to yield more credible results. In a recent study concerning yield‑related traits and drought tolerance, GWAS and QTL mapping were used to identify genomic regions associated with five 5 agronomic traits (Guo et al. 2023). Twelve loci controlling kernel number per spike, explaining up to 25.53% of the phenotypic variation, were found with GWAS and linkage analysis (Shi et al. 2017). However, genetic studies of wheat root traits have mostly used a single method, such work by as Li et al (2020), Yang et al (2021a), Ma et al (2022), and Liu et al (2019), among others. In the present study, association mapping and linkage analysis revealed genomic regions for all 12 root-related traits at the seedling stage. A total of 10 QTL were detected not only with GWAS but also with linkage mapping. Among these QTL, QDw.sxau-1D was stably detected by multiple datasets in both populations, and had a significant impact on RDW, RFW, DW, FW, and RV. Thus, this is a key QTL for root traits. Yang et al. (2021b) reported the marker AX-109966788 for root length on chromosome 7A using a DH population generated from a cross of Yangmai 16 and Zhongmai 895. This locus coincides with the physical location of QMrl.sxau-7A in the present study, indicating that two loci likely represent the same locus. Meanwhile, we identified three QTL-rich clusters associated with wheat root traits. QRd.sxau-1A was located on chromosome 1A (Table 2). This region has QTL-rich clusters for wheat root-related traits, indicating this interval may be related to wheat root architecture. QRdw.sau-1A was associated with root dry weight (Li et al. 2020) and was co-located with wsnp_Ex_c1861_3507281 for root fresh weight (Salarpour et al. 2020), and with QRL.caas-1AL (C) for root length under normal nitrogen conditions (Yang et al.2021a). QRdw.sxau-6A was located within 596.50 Mb - 602.16Mb on chromosome 6A (Table 3). Comparative analysis revealed that this locus overlaps with three loci for root length, QRL.caas-6AL (Yang et al.2021b), Xgwm169 (Ayalew et al.2017), and QMrl.sau-6A (Li et al. 2020), suggesting this region may be a QTL rich cluster for wheat root-related traits. A similar cluster was discovered via comparative analysis with locus QDw.sxau-6B co-located with QRL.caas-6BL for root length (Yang et al.2021b) and with QRTN.caas-6BL for root tip number (Yang et al.2021b). Presumably, this is due to pleiotropic effects. Research on the regulatory mechanisms of SCVs is insufficient. Therefore, next steps include analysis of candidate genes related to SCVs and their mechanism of regulation of root traits. Declarations Author contribution statement JZ and JLW designed the study. NCW and JBT performed the experiments. NCW, JJZ, BBW, LQ, XHL, and XWZ analyzed experimental results. NCW and YQH wrote the manuscript. JZ, JLW, and JJZ helped revise the manuscript. All authors read and commented on the final version for publication. Funding This project was supported by National Key Research and Development Program of China (2023YFD1201003), The Key Research and Development Project of Shanxi Province (202102140601001-1), and Graduate Education Reform and Quality Improvement Program of College of Agriculture, Shanxi Agricultural University (2023YCX01). 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China","correspondingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Li","suffix":""},{"id":287919451,"identity":"330e9b2e-3f3a-4b0c-98be-ef3a80a8971b","order_by":7,"name":"Xingwei Zheng","email":"","orcid":"","institution":"Institute of Wheat Research, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province) Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Linfen, China","correspondingAuthor":false,"prefix":"","firstName":"Xingwei","middleName":"","lastName":"Zheng","suffix":""},{"id":287919452,"identity":"f8873a0c-075b-4c1d-bcfd-16adc492f66c","order_by":8,"name":"Juanling Wang","email":"","orcid":"","institution":"Institute of Wheat Research, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province) Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Linfen, China","correspondingAuthor":false,"prefix":"","firstName":"Juanling","middleName":"","lastName":"Wang","suffix":""},{"id":287919453,"identity":"64590ee4-de9e-4a79-a989-155ed11e43ea","order_by":9,"name":"Jun zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIie3PMQrCMBTG8RcLr8ujcyfjEVJyoUiHLu3ewa3QqTh3EL2Fc0qhLtULuAiuHqBDB4MX8HUTzA8CGb4/JACe95sCd3CNYWWXJTqiwSxKYHuM8w1vL9ui1zRHWQ05wFSevydqvKUpERY1XK1oxjsjCRvdU+wSsTeBqBmJrMglCjMMSPESuDQ6JYMGkZuocdTJwWJSE5qO9RfZ5jp+zYOUp2f3mErOw5wVwfC5WN7eERPs2GPP87w/9AZFojSwVeEEbgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0842-7696","institution":"Institute of Wheat Research, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province) Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Linfen, China","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"zheng","suffix":""}],"badges":[],"createdAt":"2024-03-21 10:06:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4142603/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4142603/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-025-04847-6","type":"published","date":"2025-03-07T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54448568,"identity":"5247edbd-c7e0-422e-840d-08c219a898f1","added_by":"auto","created_at":"2024-04-10 17:17:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":542756,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypes for root traits at the seedling stage in the association panel. \u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003eb\u003c/strong\u003e represent the root phenotype under NCE and SCE, respectively. \u003cstrong\u003ec\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e represent the root phenotypic differences of cultivars under NCE / SCE. Among them, \u003cstrong\u003ec\u003c/strong\u003e represents the number of lateral roots, TRL, and number of root tips of Jinmai 65 were significantly higher at SCE than NCE. \u003cstrong\u003ed\u003c/strong\u003e represents that no significant difference in root traits between NCE and SCE in Jinmai 102. \u003cstrong\u003ee\u003c/strong\u003e represents the correlation analysis between seedling traits and adult agronomic traits\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/208776e0015bc78314d1b636.png"},{"id":54448737,"identity":"d5ab2008-1045-46bd-9150-ac25573a04de","added_by":"auto","created_at":"2024-04-10 17:25:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2915699,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of SCVs based on karyotype after ONPM # 7 FISH. \u003cstrong\u003ea\u003c/strong\u003e Blue indicates chromosomes of Chinese Spring counterstained with DAPI. \u003cstrong\u003eb\u003c/strong\u003e Green indicates BSCL135-1, BSCL135-2, and (GAA)10 modified with FAM. \u003cstrong\u003ec\u003c/strong\u003e Red indicates oligonucleotides pAs1-1, pAs1-3, pAs1-4, pAs1-6, pSc119.2–1, AFA-3, AFA-4, Grass-5S-1, and Grass-5S-2 modified with TAMRA. \u003cstrong\u003ed\u003c/strong\u003e Karyotype of Chinese Spring via merging of panels a, b, and c. \u003cstrong\u003ee\u003c/strong\u003e SCVs significantly associated with root traits.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/df723e4dec6354af761701c4.png"},{"id":54448736,"identity":"aa25407e-37f9-4f14-8671-a8510891ad04","added_by":"auto","created_at":"2024-04-10 17:25:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":602869,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of for DW and RD with multiple methods. \u003cstrong\u003ea \u003c/strong\u003erepresents the co-localization map of DW, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (A), SCE conditions (B), linkage analysis of a DH population with SNPs (C), and association analysis of SCV (D); \u003cstrong\u003eb\u003c/strong\u003erepresents the co-localization map of RD, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (E), association analysis of SNPs under SCE conditions (F-G), and association analysis with SCV (H)\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/76a60007577d60d4751c1f25.png"},{"id":54448567,"identity":"6ea4ae31-8219-40e3-ac58-8fb2f18f3ae2","added_by":"auto","created_at":"2024-04-10 17:17:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":546019,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of \u003cem\u003eQDW.saw-6A\u003c/em\u003e.\u003cstrong\u003ea\u003c/strong\u003e Idiogram of Mg6A-9 identified by Oligo-FISH.\u003cstrong\u003e b\u003c/strong\u003ePhysical map for \u003cem\u003eQDW.saw-6A\u003c/em\u003e. \u003cstrong\u003ec\u003c/strong\u003e Manhattan plot of \u003cem\u003eQDW.saw-6A\u003c/em\u003eidentified by GWAS. \u003cstrong\u003ed\u003c/strong\u003e Effect of \u003cem\u003eQDW.saw-6A\u003c/em\u003e on root related traits.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/a10dc602026817fd1b10ecfb.png"},{"id":54448571,"identity":"e7338f90-25b3-48e7-a21d-3b5c0dee88a8","added_by":"auto","created_at":"2024-04-10 17:17:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":453283,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of \u003cem\u003eQDW.saw-1D\u003c/em\u003e.\u003cstrong\u003ea\u003c/strong\u003e Manhattan plot of DW with markers located on chromosome 1D. \u003cstrong\u003eb \u003c/strong\u003ePhysical map for \u003cem\u003eQDW.saw-1D\u003c/em\u003e. \u003cstrong\u003ec \u003c/strong\u003eGenetic linkage map for \u003cem\u003eQDW.saw-1D\u003c/em\u003e. d Effect of \u003cem\u003eQDW.saw-1D\u003c/em\u003e on root-related traits.\u003c/p\u003e\n\u003cp\u003eRDW, root dry weight; DW, plant dry weight; RFW, root fresh weight; RV, root volume\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/d27e059392007e7c4b11a9f8.png"},{"id":54448574,"identity":"c345633a-3b45-489c-8328-9b9fad07a521","added_by":"auto","created_at":"2024-04-10 17:17:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":595507,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of increasing numbers of favorable alleles for stable loci on root traits using SNPs and SCV in the association panel\u003c/p\u003e\n\u003cp\u003eRFW, root fresh weight; RS, root surface-area; RV, root volume; TRL, total root length\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/e9bdb8d815a3684e44c17a6b.png"},{"id":54448572,"identity":"1255331b-c518-4da7-8e1c-4792c3f8f5c2","added_by":"auto","created_at":"2024-04-10 17:17:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":713334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e The Manhattan plot for\u003cem\u003eQDw.sxa\u003c/em\u003eu-6A; \u003cstrong\u003eb\u003c/strong\u003e Linkage disequilibrium heatmap of candidate region on chromosome 6A; \u003cstrong\u003ec\u003c/strong\u003e Transcriptome data for candidate region genes in different tissues. A, radicle; B, roots; C, axillary roots; D,shoots; E, coleoptile; F, stem; G, flag leaf; H, leaf; I, pistil; J, stamen; K, spike; L, spikelets; M, grain; N, endosperm; O, aleurone layer\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/506f329b1a39675098812750.png"},{"id":78192084,"identity":"0341fa2f-7a5e-4b98-9c69-efabdb469839","added_by":"auto","created_at":"2025-03-10 20:22:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10518088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/b32eb990-afb3-4f92-8fdd-134faf4d7a4a.pdf"},{"id":54448575,"identity":"d9945b22-de0a-475c-880d-524c2288576d","added_by":"auto","created_at":"2024-04-10 17:17:44","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16675385,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2024.3.20.docx","url":"https://assets-eu.researchsquare.com/files/rs-4142603/v1/232be95d3681bc339a1ca5d9.docx"}],"financialInterests":"","formattedTitle":"Genetic dissection for seedling root-related traits using multiple-methods in bread wheat (Triticum aestivum L.)","fulltext":[{"header":"Key Message","content":"\u003cp\u003eSeveral QTL and SCVs for seeding root traits were identified using multiple methods, and these can be used to breed for improved root systems in wheat.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is a major crop worldwide, accounting for about 35% of the staple food (Han et al. 2019). Roots absorb water and nutrients and directly affect growth and yield of the aboveground plant parts (Atkinson et al. 2015). Various root architecture features are associated with different functions. For example, maximum root length affects acquisition of water (Wasson et al.2012), and root number, diameter and surface area affect nutrients uptake (Colombi and Walter 2017; Meister et al.2014;\u0026nbsp;Courtois et al. 2009). Root biomasses are essential for the balance between aboveground biomass and root biomass, which in turn affects yield (Bettembourg et al. 2017;\u0026nbsp;Cormier et al. 2016).\u0026nbsp;Root systems respond to different growing environments by changing their morphology and distribution in the soil, which in turn alter water and fertilizer use efficiency, lodging resistance and\u0026nbsp;heat tolerance\u0026nbsp;in wheat (Li\u0026nbsp;et al. 2022;\u0026nbsp;Uga\u0026nbsp;et al. 2013). Thus, understanding the genetic basis of root architecture could enhance wheat breeding for many important traits.\u003c/p\u003e\n\u003cp\u003eBecause measuring\u0026nbsp;root-related phenotypes is difficult, there are\u0026nbsp;rarely and lagging\u0026nbsp;genetic studies of root architecture.\u0026nbsp;Commonly used methods for investigating root traits include seedling stage measurements from plants grown under controlled conditions and field evaluations at the adult stage (Atkinson et al. 2019). The methods to study roots at the adult stage include excavation, basket, and use of a minirhizotron.\u0026nbsp;These approaches have great damage to root system, high labor intensity and slow speed\u0026nbsp;of measurement, which is not suitable for genetic analysis of large mapping populations (Vamerali et al. 2012; Zheng et al. 2019). However, seedling-stage measurement is fast, simple, and highly correlated with root morphology and distribution at the adult stage. Consequently, analysis of roots at the seedling stage has been widely used in genetic studies of root development in wheat. Controlled environment root phenotyping methods include hydroponics (Ayalew et al.2015), clear pots\u0026nbsp;(Richard et al.2015), and pots filled with soil (Cao et al. 2014). Ma et al (2022) found significant correlation of root traits between outdoor pot culture and indoor pot culture. However, root traits, such as total root length, root diameter and root tip number\u0026nbsp;were more variable in outdoor pot culture. Wang et al (2023a) found significant differences in root traits between pure water hydroponic versus Hoagland nutrient solution hydroponic culture. With Hoagland nutrient solution, root length was short and root diameter was greater. Under pure water culture, the main root was longer, and the number of lateral roots was higher. However, it is unclear if there are differences in root traits between hydroponics and soil culture at the seedling.\u003c/p\u003e\n\u003cp\u003eLinkage mapping and association analysis are the main approaches for quantitative trait loci (QTL) localization. In the seedling stage,\u0026nbsp;QTL for root-related traits have been localized by linkage analysis on all wheat chromosomes. With RIL and DH population, a total of 51 related QTL were detected that accounted for 1.54% to 31.99% of the phenotypic variation in root traits at the seedling stage (Kabir et al. 2015). Also using a RIL population, thirteen QTL on chromosomes 1B, 2D, and 5A accounted for 9.00% to 17.00% of the phenotypic variation (Ayalew et al. 2017). Yang et al. (2021a) identified 51 QTL for root architecture and biomass-related traits at the seedling stage with a DH population,\u0026nbsp;explaining 4.66%-19.12% of the phenotypic variation. Linkage analysis rapidly detects loci, genetic positions, and effects of target traits.\u0026nbsp;However, linkage analysis only detects relatively\u0026nbsp;favorable\u0026nbsp;allelic variations in both parents. Association analysis can reduce the limitations of bi-parental QTL mapping.\u0026nbsp;For example, eighteen stable QTL for five seedling-stage root traits were identified in an association panel of 189 varieties, explaining 8.68%-14.07% of phenotypic variation (Wang et al.2023a).\u0026nbsp;Ma et al (2022) investigated nine root traits at the seedling stage using an association panel of 388 varieties, and a total of 36 associated QTL were identified \u0026nbsp;explaining1.23%-6.77% of the phenotypic variance. A study using association analysis of a diverse population of 165 wheat cultivars identified 32 stable QTL for seedling root-related traits, which explained 8.80%-15.60% of the phenotypic variation (Liu et al. 2019). Association analysis can simultaneously detect multiple alleles at the same location. However, false positive results are common with association analysis. Therefore, the combination of association analysis and linkage analysis more accurately locates the related loci for complex traits. This method has been widely used for QTL mapping and gene identification of above-ground traits such as plant height, spike length, and thousand grain weight (Shi et al.2017; Guo et al.2023). In contrast, there are few studies combining\u0026nbsp;association analysis and linkage analysis\u0026nbsp;for root-related traits.\u0026nbsp;In terms of gene cloning,\u0026nbsp;a small number of genes for root-related traits have been reported, including\u003cem\u003e\u0026nbsp;TaSRL1\u003c/em\u003e(Zhuang et al. 2021), \u003cem\u003eTabHLH123\u003c/em\u003e(Wang et al. 2023b), \u003cem\u003eTaRNAC1\u003c/em\u003e(Chen et al. 2018) and \u003cem\u003eTaNAC69-1\u003c/em\u003e(Chen et al. 2016). These studies are insufficient for exploring the complex regulatory mechanisms of the root system. In addition, the regulatory mechanisms of root traits are involved in molecular, pathway, network, and cellular levels of complexity. Aside from QTL/genes mapping, the regulatory roles of epigenetic modifications and chromosomal structural variations must be studied (Dowell\u0026nbsp;et al. 2010).\u003c/p\u003e\n\u003cp\u003eChromosomal structural variations are large length sequence changes and positional changes on the genome, including presence/absence variations (PAV), copy number variations (CNV), inversions, translocations, and complex rearrangements (Gabur et al. 2019).\u0026nbsp;Wheat is a heterologous hexaploid, with a large and complex genome\u0026nbsp;that developed many SCVs during its evolution and selection\u0026nbsp;(Dvorak et al. 2018). SCVs play an essential role in wheat growth and environmental adaptation. For example, Zhao et al. (2023) found three SCVs on chromosomes 4A, 5A, and 7B, which were significantly associated with wheat grain length, grain width, and spike length. Zhao et al. (2022) analyzed 12 agronomic traits of wheat, including 1000-grain weight, grain length, and grain width, and identified 29 significantly associated SCVs, with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging from 1.10 % to 37.19 %.\u003c/p\u003e\n\u003cp\u003eThere have been some drawbacks with genetic studies of wheat root systems including: (1) uncertainty if hydroponic and soil culture conditions at the seedling stage are comparable, (2) only either linkage mapping or association analysis was used, and the major loci are rarely identified, and (3) the studies of chromosome level have not been reported. The present study used an association panel and a doubled haploid (DH) population to investigate 12 root-related traits under both hydroponic and soil culture conditions. Using SNPs and SCVs combined with association and linkage analysis, stable loci related to root traits in wheat seedlings were identified. On this basis, effects of stable loci were studied, which provided new insights into the genetics of wheat root-related traits.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e2.1. Plant materials\u003c/p\u003e\n\u003cp\u003eAn association panel of 282 wheat accessions from Shanxi Province, China, was used in the present study. These genotypes include 126 irrigated and 115 dryland modern wheat cultivars. The other 41 materials were landraces from Shanxi Province selected from the Chinese wheat core collection (Zheng\u0026nbsp;et al.2022). The DH population of 201 lines was generated from a cross between Jinmai 47 \u0026times; Jinmai 84, and the root traits of the two parents differed significantly.\u003c/p\u003e\n\u003cp\u003e2.2. Root phenotyping of seedlings\u003c/p\u003e\n\u003cp\u003eTwenty seeds of uniform size were surface sterilized by soaking in 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 30 min, rinsed several times with sterilized water, and placed in Petri dishes with one sheet of filter paper moistened with 5 mL of distilled water.\u0026nbsp;The dishes were kept in a dark incubator at 20 \u0026deg;C until germination.\u0026nbsp;After germination, the seeds were cultured under two conditions. In the NCE (nutrient solution culture experiment) method, five seeds with uniform germination were kept in Petri dishes for 6 days\u0026nbsp;before being transferred to Hoagland nutrient solution (pH=6.0) and cultured for 14 days. The nutrient solution was continuously aerated and was replaced every 3 days (Wang et al. 2023a). The NCE was repeated two times. In the SCE (soil culture experiment) method, five uniformly germinated seeds of each variety were transferred into plastic pots (diameter, 7 cm; height, 8 cm) filled with vermiculite and 100 mL water\u0026nbsp;and allowed to grow\u0026nbsp;for 20 days (Zhao et al. 2021). The SCE was repeated three times. Two experiments were placed in a growth chamber with the light intensity of 500 \u0026micro;mol m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e,\u0026nbsp;a humidity of 70 %, with a 16 h/8 h light/ darkness photoperiod at 20 \u0026deg;C/16 \u0026deg;C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwelve root-related traits were measured. Root images of three seedlings were captured using a Microtek Scanmakeri 800 plus scanner. MRL (maximum root length) was measured. RN (root number) was estimated from the images, and TRL (total root length), RS (root surface area), RV (root volume) and RD (root diameter) were measured using a Win-RHIZO system. Six biomass-related indicators were measured, including RFW (root fresh weight), SFW (shoot fresh weight), FW (plant fresh weight), RDW (root dry weight), SDW (shoot dry weight), and DW (plant dry weight).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.3. Field experiments\u003c/p\u003e\n\u003cp\u003eSix field experiments with the association panel were planted, three in the experimental station of Yaodu District, Linfen City, Shanxi Province of China (P1:36\u0026deg;48\u0026apos;N, 111\u0026deg;30\u0026apos;E) in 2020-2021 and three in the experimental station of Yanhu District, Yuncheng City, Shanxi Province of China (P2:35\u0026deg;20\u0026apos;N, 110\u0026deg;59\u0026apos;E) in 2020-2021. Each planting was considered a different environment. With the method of Qiao et al. (2022), 13 agronomic traits at the adult stage were assessed, including PH (plant height), SL(spike length), LL (flag leaf length), LW (flag leaf width), SN (spike number), BSS (base sterile spikelet number per spike), TSS (top sterile spikelet number per spike), GNS (grain number per spike), GL (grain length), GW (grain width), GT (grain thickness), TGW(1000-grain weight), and HD (heading data).\u003c/p\u003e\n\u003cp\u003e2.4. Genomic DNA extraction and genotyping\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted using the CTAB method. High-throughput genotyping was performed using a 16K SNP panel from the MOL-BREEDING\u0026nbsp;Company.\u0026nbsp;After eliminating markers with an allele frequency \u0026lt; 0.05, \u0026gt; 10% missing data, or \u0026gt; 20% heterozygosity, 9793 high-quality SNPs\u0026nbsp;were identified in the association panel for GWAS (Zheng et al. 2022). Briefly, markers with no polymorphisms between parents, severely distorted segregations, and missing rates greater than 15% were removed in the subsequent linkage analysis. Finally, 1373 SNPs were selected to construct the linkage map of the DH population.\u003c/p\u003e\n\u003cp\u003e2.5. FISH experiments and karyotype analysis\u003c/p\u003e\n\u003cp\u003eChromosome preparation and FISH used the procedure of Zhao et al (2023). Under an Olympus BX60 (Olympus Inc, Japan) microscope, the images were captured with a SPOT CCD (SPOT Cooled Color Digital, Olympus-DP80)\u0026nbsp;camera. The cells with a complete number of chromosomes and good dispersion were selected for karyotype analysis with Photoshop v6.0 (Adobe Inc. USA).\u003c/p\u003e\n\u003cp\u003e2.6. Genome-wide association study of the association panel\u003c/p\u003e\n\u003cp\u003eThe MLM model was used for association analyses between root-related traits and markers. SNP marker-trait associations (MTAs) were tested with TASSEL 5.0 software and MTAs with a threshold of \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001 were considered as significant MTAs (Zheng et al 2022). SCV MTAs were tested with TASSEL 5.0 and MTAs with a threshold of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 were considered significant (Zhao et al.2022). MTAs repeatedly detected in two or more single environments ware considered to be stably associated loci. A Manhattan plot was generated via RStudio software to visualize the GWAS results. The extended region where the LD value between nearby SNPs and peak SNP decayed to\u003cem\u003e\u0026nbsp;r\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.2 was defined as the local LD based the QTL interval.\u003c/p\u003e\n\u003cp\u003e2.7. Linkage analysis of DH population\u003c/p\u003e\n\u003cp\u003eQTL were detected using WinQTLCart2.5 (https://brcwebportal.cos.ncsu.edu /qtlcart/WQTLCart. htm) based the CIM method. QTL were considered significant at a threshold of LOD \u0026gt; 2.5. QTL repeatedly detected in two or more single environments were considered stably QTL. QTL either less than 1 cM apart or sharing common flanking markers were treated as a single locus and named according to McCouch et al. (1997).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.8. Gene analysis of functional interval\u003c/p\u003e\n\u003cp\u003eGenes within the target region were identified using the genome browser (JBrowse) the WheatOmics data website (Ma et al. 2021). The sequences of common wheat genes were retrieved based on the intervals of major QTL identified from https://urgi.versailles.inra.fr/download/iwgsc/ IWGSC_RefSeq_ Annotations/v1.0/ (Zheng et al. 2019). Functional annotation of genes in segments were done with the Gene Ontology (GO) database using the R package cluster Profiler. The RNA-seq of genes within the target region were retrieved from Wheat Expression Browser (http://www.wheat-expression.com/).\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Phenotypic variation\u0026nbsp;of the association panel\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe analyzed the phenotypes of 12 traits in the association panel across all three environments under NCE conditions and across all four environments under SCE conditions. The frequency distributions of root-related traits were continuous and normally distributed in both NCE and SCE (Fig.S1). Differences in culture conditions affect root traits. For example, the RDW, RFW, TRL, RS, RV, and RN under SCE were higher than NCE, whereas RD was lower. Under NCE, the range of variation for RDW was the largest, from 2.43 mg to 9.10 mg. The RDW of Jinmai 72, Chang 7016 and Tai 113 were more sensitive to the change in culture conditions. Under SCE, the variation range of TRL was the largest, from 58.19cm to 156.81cm. The TRL of Jinmai 65, Jinmai 68 and Baitumai were more sensitive to the change in culture conditions, indicating varieties were greatly affected by environment. The relative variation range of RD was the smallest under the two conditions, from 0.44 cm to 0.50 cm under NCE and from 0.37 cm to 0.53 cm under SCE.\u0026nbsp;The RD variation of Jinmai 16, Jinchun 15, and Jinmai 102 between NC and SC was less, indicating that these varieties were less affected by changes in culture conditions.\u003c/p\u003e\n\u003cp\u003eThe phenotype was analyzed according to the type of variety.\u0026nbsp;Under NCE, the CV of root traits ranged from 2.29% to 19.45% for irrigated cultivars, from 2.07% to 17.54% for dryland cultivars, and from 1.73% to 18.48% for landraces. Under SCE, the CV of root traits ranged from 5.94% to 14.60% for irrigated cultivars, from 3.04% to 13.23% for dryland cultivars, and from 2.58% to 14.21% for landraces (Table.S1). In general, the root phenotype of the seedling stage was various and had a wide range of genetic variation.\u0026nbsp;Among the variety types, phenotypic variation was the largest in irrigated cultivars, less in dryland cultivars, and lowest in landraces.\u003c/p\u003e\n\u003cp\u003eDifferent culture conditions led to different phenotypes of root traits at the seedling stage. The root biomass (RDW and RFW), TRL, RS, RV, and RN were significantly lower under NCE than under SCE, while RD was significantly higher than under SCE (Table.1). Compared with SCE, the variation in root traits was more abundant under NCE.\u0026nbsp;The CV of root traits at the seedling stage in NCE was between 2.25 % and 20.80 %, among which the CV of RDW was the greatest and the CV of RD was the least (Table.1). Under SCE, the CV of root traits at seedling stage was between 5.61% and 15.03 %, among which the CV of SDW was the greatest and the CV of RDW was the least (Table.1).\u0026nbsp;ANOVA showed that there was no significant difference in MRL between the two culture conditions, while the differences between NCE and SCE for other traits were significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.1.\u003c/strong\u003e Phenotypes for root traits at the seedling stage in the association panel.\u0026nbsp;\u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003eb\u003c/strong\u003e represent the root phenotype under NCE and SCE, respectively. \u003cstrong\u003ec\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e represent the root phenotypic differences of cultivars under NCE / SCE. Among them, \u003cstrong\u003ec\u003c/strong\u003e represents the number of lateral roots, TRL, and number of root tips of Jinmai 65 were significantly higher at SCE than NCE. \u003cstrong\u003ed\u003c/strong\u003e represents that no significant difference in root traits between NCE and SCE in Jinmai 102. \u003cstrong\u003ee\u003c/strong\u003e represents the correlation analysis between seedling traits and adult agronomic traits\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNCE, nutrient solution culture experiment; SCE, soil culture experiment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRL, maximum root length; RFW, root fresh weight; RS, root surface-area; RV, root volume\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.1.\u003c/strong\u003e A\u0026nbsp;Statistical analysis of 12 root-related traits at the seedling stage under NCE and SCE conditions\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"531\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.312617702448211%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.548022598870055%\" colspan=\"3\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.79472693032015%\" colspan=\"3\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.39423076923077%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.78846153846154%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.298076923076923%\"\u003e\n \u003cp\u003eCV %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.432692307692307%\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.509615384615383%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\"\u003e\n \u003cp\u003eCV\u003cem\u003e\u0026nbsp;\u003c/em\u003e%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eSDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e13.41\u0026ndash;15.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e14.31\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e9.33\u0026ndash;22.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e15.50\u0026plusmn;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e15.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e2.43\u0026ndash;9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e4.51\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e20.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e9.85\u0026ndash;16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e10.93\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e16.22\u0026ndash;23.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e18.82\u0026plusmn;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e18.59\u0026ndash;45.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e26.43\u0026plusmn;3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eSFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e142.88\u0026ndash;172.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e153.97\u0026plusmn;4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e71.20\u0026ndash;170.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e110.32\u0026plusmn;15.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e13.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eRFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e50.68\u0026ndash;71.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e58.98\u0026plusmn;4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e76.50\u0026ndash;152.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e113.95\u0026plusmn;13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e11.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e196.49\u0026ndash;244.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e212.91\u0026plusmn;8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e148.71\u0026ndash;313.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e224.27\u0026plusmn;27.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eMRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e13.65\u0026ndash;19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e16.29\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e13.72\u0026ndash;18.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e16.20\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eTRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e33.43\u0026ndash;58.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e43.77\u0026plusmn;4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e10.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e58.19\u0026ndash;156.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e104.10\u0026plusmn;14.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003ecm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e6.90\u0026ndash;10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e8.28\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e7.53\u0026ndash;16.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e12.21\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e12.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003ecm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u0026ndash;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u0026ndash;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003ecm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e0.44\u0026ndash;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u0026ndash;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.344632768361582%\"\u003e\n \u003cp\u003eRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.967984934086629%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.760828625235405%\" valign=\"top\"\u003e\n \u003cp\u003e2.68\u0026ndash;5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.935969868173258%\" valign=\"top\"\u003e\n \u003cp\u003e5.10\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.851224105461393%\" valign=\"top\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.0075329566855%\" valign=\"top\"\u003e\n \u003cp\u003e3.37\u0026ndash;6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.500941619585687%\" valign=\"top\"\u003e\n \u003cp\u003e5.16\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.286252354048964%\" valign=\"top\"\u003e\n \u003cp\u003e8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.344632768361582%\" valign=\"top\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSDW, shoot dry weight; RDW, root dry weight; DW, plant dry weight; SFW, shoot fresh weight; RFW, root fresh weight; FW, plant fresh weight; MRL, maximum root length; TRL, total root length; RS, root surface-area; RV, root volume; RD, root diameter; RN, root number\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCV, coefficient of variation\u003c/p\u003e\n\u003cp\u003e**, significant difference at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *, significant difference at\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003eNCE, nutrient solution culture experiment; SCE, soil culture experiment\u003c/p\u003e\n\u003cp\u003e3.2. Relationship between seedling root traits and agronomic traits in the association panel\u003c/p\u003e\n\u003cp\u003eRoot traits at the seedling stage often reflect root morphology and distribution at the adult stage.\u0026nbsp;The correlation between 12 root-related traits under SCE at the seedling stage and 13 agronomic traits at the adult stage was analyzed.\u0026nbsp;RDW, RFW, RS, RV, SDW, SFW, DW, and FW were significantly correlated with more than 10 agronomic traits (Fig. S2a).\u0026nbsp;Under the NCE condition at the seedling stage, RFW, RDW, RS, and RV were significantly correlated with agronomic traits, while SDW, DW, SFW, and FW were not significantly correlated with most of agronomic traits (Fig. S2b). On this basis, the correlation between the same traits under SCE and NCE conditions at the seedling stage was compared, and RFW, RDW, RS, and RV were significantly correlated under the two culture conditions (Fig. S2c).\u0026nbsp;This result indicated that the method of hydroponic identification of RFW, RDW, RS, and RV at the seedling stage could replace the method of soil culture to further study the relationship with agronomic traits, while SDW, DW, SFW, and FW were\u0026nbsp;more accurately assessed\u0026nbsp;under soil culture conditions.\u003c/p\u003e\n\u003cp\u003eTo study the relationship between root traits at the seedling stage and agronomic traits at the adult stage of different types of varieties,\u0026nbsp;correlation analyses were performed between root-related traits at the seedling stage and agronomic traits of\u0026nbsp;irrigated cultivars and dryland cultivars under SCE conditions. There were significant correlations between root traits at the seedling stage and PH, TGW, GL, GW, and GT at the adult stage in dryland cultivars, but the correlation was lower in irrigated cultivars (Table S2). A comparison of root phenotypes of irrigated cultivars and dryland cultivars showed that the MRL of dryland cultivars at the seedling stage was slightly longer than that of irrigated cultivars (Table S1). Therefore, agronomic traits such as PH, TKW, GL, GW, and GT were compared between irrigated cultivars and dryland cultivars. The PH, TKW and GW of dryland cultivars were significantly higher than those of irrigated cultivars, which indicated that the longer root length of dryland cultivars may have promoted more biomass accumulation and yield formation under the same growing conditions.\u003c/p\u003e\n\u003cp\u003e3.3. Association analysis of association panel with SNP markers\u003c/p\u003e\n\u003cp\u003eAssociation analysis between 12 root-related traits and SNP markers was performed using the MLM model. Under NCE conditions, a total of 29 stable loci distributed on 11 chromosomes were identified, which explained 5.08% to 23.25% of the phenotypic variation. Thirteen of these loci have been reported to be associated with root architecture (Fig.S3,Table.2). The significance level of \u003cem\u003eQDw.sxau-1B.2\u003c/em\u003e ranged from 9.02 to 11.90 in all three environments, with a\u003cem\u003e\u0026nbsp;R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of 16.65% to 23.25%. Furthermore, \u003cem\u003eQDw.sxau-2A.1\u003c/em\u003e was stably detected in all three environments, with \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e ranging from 5.98% to 9.02%(Fig.S3,Table.2).\u003c/p\u003e\n\u003cp\u003eUnder SCE conditions, GWAS identified 23 loci for 12 seedling traits in different environments on 11 chromosomes, including\u0026nbsp;chromosomes 1A, 1B and 2B, which explained 5.56% - 17.02% of the phenotypic variation. Five of these loci have been reported previously to be associated with root architecture. The \u003cem\u003eQRdw.sxau-2D.2\u0026nbsp;\u003c/em\u003ehad the highest phenotypic variation rate, with a \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e of up to 17.02% (Fig.S3,Table.2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.2.\u003c/strong\u003e Characteristics of significant loci related to root traits for wheat under two culture conditions.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\"\u003e\n \u003cp\u003eLoci Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\"\u003e\n \u003cp\u003eCulture system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\"\u003e\n \u003cp\u003eChr.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\"\u003e\n \u003cp\u003ePosition (Mb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\"\u003e\n \u003cp\u003e-log10(\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eSDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSdw.sxau-2A.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e305.69-305.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.17-3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.80-6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSdw.sxau-2A.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e747.08-747.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.23-3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.99-6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eLi et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSdw.sxau-2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e135.69-135.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.56-4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e7.03-8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSdw.sxau-3D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e602.07-602.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.88-4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.62-7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eSalarpour et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSdw.sxau-6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e597.62-597.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.44-3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.75-6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021b Ayalew et al. 2017 \u0026nbsp;Li et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRdw.sxau-2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e191.20-194.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.18-3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.40-6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRdw.sxau-2D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e579.39-585.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.92-7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e8.40-16.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRdw.sxau-2D.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e586.95-593.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e4.11-7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e8.82-17.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRdw.sxau-5D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e359.10-359.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e4.91-6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e10.62-13.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRdw.sxau-6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE/NCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e596.50-602.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.43-6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.99-14.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021b Ayalew et al. 2017 \u0026nbsp;Li et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRdw.sxau-6B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e512.21-512.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.51-4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.98-8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-1B.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e110.64-110.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e5.54-7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e9.96-12.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-1B.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e135.81-135.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e9.02-11.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e16.65-23.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-1D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e35.79-43.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.72-5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e7.24-16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-2A.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e305.69-305.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.16-5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.98-9.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-2A.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e327.21-327.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.89-5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.66-10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e771.81-771.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e4.94-6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e9.15-13.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-2D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e584.52-589.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.56-6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.94-14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-4B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e526.96-526.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e6.20-6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e11.47-12.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-5D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e450.20-450.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e5.92-6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e10.54-11.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE/NCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e596.50-602.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.58-6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.56-13.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021b Ayalew et al. 2017 \u0026nbsp;Li et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQDw.sxau-6B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e714.44-714.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.02-3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.94-8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eSFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e514.94-514.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.35-3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.18-6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eHuang et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e305.69-305.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.44-3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.92-7.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e135.69-135.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.03-3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.36-7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-3D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e602.07.602.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.08-3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.49-5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eSalarpour et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-5A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e100.59-109.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.08-3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.90-6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e609.31-609.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.05-4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.80-7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021b Ayalew et al. 2017 \u0026nbsp;Li et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQSfw.sxau-7B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e169.59-169.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.23-3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.20-7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eRFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRfw.sxau-5B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e138.30-138.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.07-3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.04-5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eFW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQFw.sxau-2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e305.69-305.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.00-3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.08-7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQFw.sxau-2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e135.69-135.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.13-3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.66-7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQFw.sxau-3D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e602.07-602.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.28-3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.69-6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eSalarpour et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQFw.sxau-5A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e382.13-382.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.02-4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.81-8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQFw.sxau-6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e609.31-609.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.23-3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.31-7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021b Ayalew et al. 2017 \u0026nbsp;Li et al. 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eMRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQMrl.sxau-1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e38.01-38.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.16-3,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e7.76-8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQMrl.sxau-4B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e37.70-37.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.11-3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.25-5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eZheng et al. 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQMrl.sxau-7A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e709.98-709.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.21-5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e8.83-11.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRv.sxau-2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e55.93-55.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.52-3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.43-6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eYang et al. 2021a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRv.sxau-3B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e318.27-318.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.11-3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.45-7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRd.sxau-1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e592.35-592.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.31-5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.55-10.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eLi et al. 2020\u003c/p\u003e\n \u003cp\u003eSalarpour et al. 2020\u003c/p\u003e\n \u003cp\u003eYang et al. 2021a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRd.sxau-1B.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e408.45-408.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.10-3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.42-7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRd.sxau-1B.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE/NCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e492.38-492.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.01-3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.04-6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRd.sxau-4A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e450.89-450.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.08-4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.25-8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRd.sxau-5A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e502.69-502.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.42-3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e7.17-7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRd.sxau-6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eNCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e546.08-546.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.20-3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e5.44-5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003eRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRn.sxau-1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e367.05-367.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.10-3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.34-6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRn.sxau-5A.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e394.56-394.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.45-3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e7.13-7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003eAyalew et al. 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.95741556534508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.831130690161528%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eQRn.sxau-5A.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003eSCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.607929515418502%\" valign=\"top\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.537444933920705%\" valign=\"top\"\u003e\n \u003cp\u003e684.73-684.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45374449339207%\" valign=\"top\"\u003e\n \u003cp\u003e3.00-3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.600587371512482%\" valign=\"top\"\u003e\n \u003cp\u003e6.70-7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.474302496328928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSDW, shoot dry weight; RDW, root dry weight; DW, plant dry weight; SFW, shoot fresh weight; RFW, root fresh weight; FW, plant fresh weight; MRL, maximum root length; RV, root volume; RD, root diameter; RN, root number\u003c/p\u003e\n\u003cp\u003eNCE, nutrient solution culture experiment; SCE, soil culture experiment\u003c/p\u003e\n\u003cp\u003e3.4. Distribution and association analysis of SCVs in the association panel\u003c/p\u003e\n\u003cp\u003eONPM # 7, a powerful tool for studying structural variation diversity and genetic effects, is composed of 12 sequences, and was used to identify PAVs/CNVs alleles at 202 SCVs loci\u0026nbsp;across all 21 wheat chromosomes (Zhao et al. 2023). A total of 74 polymorphic SCVs were identified by FISH in the association panel, which were located on 17 chromosomes, including 1A, 1B and 1D (Zhao et al. 2022).\u0026nbsp;In the DH population, 13 polymorphic SCVs located on 11 chromosomes were identified by FISH. In the association panel, GWAS identified 9 and 26 SCVs under NCE and SCE conditions, respectively (Table S3). These significant SCVs were distributed on 12 chromosomes, explaining 4.48% - 11.76% of the phenotypic variation. Thus, SCVs have important effects on root architecture.\u003c/p\u003e\n\u003cp\u003eUnder NCE conditions,\u0026nbsp;three SCVs were detected in\u0026nbsp;three\u0026nbsp;datasets.\u0026nbsp;The highest \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e value (8.13%) was found for the association between Mg1B-1 and RFW, which was confirmed in all three environments. Mg1B-1\u0026nbsp;and Mr1B-3\u0026nbsp;had significant effects on RFW, FW, TRL, RV, and RS , with \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e ranging from 5.74% to 9.07%. In addition, Mg2B-9 was significantly associated with DW, Mg1B-1 and Mr1B-3 were significantly associated with RS, and Mr1B-3 was significantly associated with TRL in all three environments, with \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e ranging from 4.55% to 9.07%(Fig.3, Table S3).\u003c/p\u003e\n\u003cp\u003eUnder SCE conditions,\u0026nbsp;twelve SCVs were detected in three or more datasets.\u0026nbsp;The highest\u003cem\u003e\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e ranging from 10.86% to 11.76% were found for the association between Mr5B-11 and RDW. Mr1B-3 was significantly associated with 10 traits simultaneously, including FW, TRL, RS, RV and RN, explaining 5.77% - 9.86% of phenotypic variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, several SCVs were stably detected in three or more environments, including Mg1D-2 associated with DW, Mr1A-1 associated with MRL, Mg2B-12 associated with RFW, Mr1D-5 associated with RV, Mg1D-2, Mg2B-6, Mg6A-10, Mr6A-2 and Mg6A-9 associated with SDW, and Mr3A-3 and Mr3A-4 associated with SFW (Fig.3, Table S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.2.\u0026nbsp;\u003c/strong\u003eIdentification of SCVs based on karyotype after ONPM # 7 FISH. \u003cstrong\u003ea\u003c/strong\u003e Blue indicates chromosomes of Chinese Spring counterstained with DAPI. \u003cstrong\u003eb\u003c/strong\u003e Green indicates BSCL135-1, BSCL135-2, and (GAA)10 modified with FAM. \u003cstrong\u003ec\u003c/strong\u003e Red indicates oligonucleotides pAs1-1, pAs1-3, pAs1-4, pAs1-6, pSc119.2\u0026ndash;1, AFA-3, AFA-4, Grass-5S-1, and Grass-5S-2 modified with TAMRA. \u003cstrong\u003ed\u003c/strong\u003e Karyotype of Chinese Spring via merging of panels a, b, and c. \u003cstrong\u003ee\u003c/strong\u003e SCVs significantly associated with root traits.\u003c/p\u003e\n\u003cp\u003e3.5. Linkage analysis of SNPs and SCVs analysis in the DH population\u003c/p\u003e\n\u003cp\u003eTo comprehensively and systematically verify the results of GWAS in the association panel, QTL mapping was performed on 201 lines using the DH population. Twenty-one stable QTL were detected by linkage analysis of 12 seedling root traits (Table S4). Five QTL were found for MRL, explaining 5.11 % -11.03 % of the phenotypic variance. The\u0026nbsp;phenotypic variation explanation rate of \u003cem\u003eQMrl.sxau-6B.1\u003c/em\u003e was the highest.\u0026nbsp;\u003cem\u003eQTrl.sxau-6B.1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eQTrl.sxau-6B.2\u003c/em\u003e explained 11.34-12.34 % of the phenotypic variation for TRL. \u003cem\u003eQRv.sxau-5A\u003c/em\u003e was detected for RV, explaining up to 15.01 % of the phenotypic variation. \u003cem\u003eQRd.sxau-1B.3\u0026nbsp;\u003c/em\u003eand \u003cem\u003eQRd.sxau-4D\u003c/em\u003e explained 8.51%\u0026ndash;17.51% of the phenotypic variance for RD. In addition, seven QTL, including \u003cem\u003eQRfw.sxau-6B.2\u003c/em\u003e, \u003cem\u003eQMrl.sxau-6B.1\u003c/em\u003e, and \u003cem\u003eQRd.sxau-4D\u003c/em\u003e, have been reported to be associated with root structure (Table S4). The\u003cem\u003e\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/em\u003eof six QTL were higher than 10 %, including \u003cem\u003eQMrl.sxau-6B.1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;QRv.sxau-5A\u0026nbsp;\u003c/em\u003eand \u003cem\u003eQRd.sxau-4D\u003c/em\u003e, were identified as major and stable QTL in multiple environments (Table S4). Among these QTL, \u003cem\u003eQRv.sxau-5A\u003c/em\u003e, \u003cem\u003eQTrl.sxau-6B.1\u003c/em\u003e, \u003cem\u003eQTrl.sxau-6B.2\u003c/em\u003e and \u003cem\u003eQMrl.sxau-6B.1\u003c/em\u003e had additive effects on root-related traits such as maximum root length, total root length, root surface area and root biomass (Fig. S4).\u003c/p\u003e\n\u003cp\u003eThe SCV of polymorphic chromosomes from the DH population with positive impact on root-related traits were PAV.2A.1+2 , PAV.2A.1, PAV.2A.2, 1BL/1RS, CNV.4B, and PAV.6B. Each SCV was associated with one or more root traits during the seedling stage. PAV.2A.1+2 was significantly associated with SDW, DW, MRL, RDW, and RS. PAV.2A.1 was significantly associated with RDW, MRL, TRL, and RS. PAV.2A.2 was significantly associated with SDW, DW, MRL, and RV. 1BL/1RS was significantly associated with SDW, DW, MRL, TRL, and RD. CNV.4B was significantly associated with TRL and SDW. PAV.6B was significantly associated with SDW, SFW, FW, MRL, and TRL. Thus, SCV had a significant effect on root-related traits.\u003c/p\u003e\n\u003cp\u003e3.6. Co-location analysis of multiple methods\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.6.1\u0026nbsp;Co-localization analysis of SCE and NCE in association panel\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Three stably loci, \u003cem\u003eQRdw.sxau-6A\u003c/em\u003e, \u003cem\u003eQRd.sxau-1B.2\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;QDw.sxau-6A\u003c/em\u003e, were detected in both the SCE and NCE\u0026nbsp;conditions\u0026nbsp;based on SNP marker association analysis.\u003cem\u003e\u0026nbsp;QRdw.sxau-6A\u003c/em\u003e was composed of four significant MTAs, which were in the 596.50-602.16 Mb physical interval on chromosome 6A, with the \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values ranging from 5.99 to 14.49%. \u003cem\u003eQDw.sxau-6A\u003c/em\u003e was also co-located in the same genetic interval with a \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e value range of 5.56%-13.60% for four MTAs (Fig.3, Table.2). This genetic interval was also reported to be significantly correlated with root length. In addition, \u003cem\u003eQRd.sxau-1B.2\u003c/em\u003e was located at 492.38-492.75Mb on chromosome 1B, explaining 6.04%-6.16% of phenotypic variation, and has not been reported previously.\u003c/p\u003e\n\u003cp\u003eMr1B-3, Mr3A-3 and Mr3A-4 were identified under both NCE and SCE conditions based on SCV association analysis, explaining 4.74%\u0026ndash;9.07% of the phenotypic variation (Table S3). Mr1B-3 was located on chromosome 1B at 69.79 Mb and had effects on RFW, RS, RV, TRL, and FW. In addition, Mr3A-3 at 566.23Mb and Mr3A-4 at 685.43Mb on chromosome 3A explained 4.74-5.38% of the phenotypic variation，which were significantly associated with TRL in four datasets. These results showed that SCVs were positively associated with root-related traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.3.\u003c/strong\u003e Identification of for DW and RD with multiple methods. \u003cstrong\u003ea\u0026nbsp;\u003c/strong\u003erepresents the co-localization map of DW, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (A), SCE conditions (B), linkage analysis of a DH population with SNPs (C), and association analysis of SCV (D); \u003cstrong\u003eb\u003c/strong\u003e represents the co-localization map of RD, from the outermost to the innermost circle: association analysis with SNPs under NCE conditions (E), association analysis of SNPs under SCE conditions (F-G), and association analysis with SCV (H)\u003c/p\u003e\n\u003cp\u003eDW, plant dry weight; RD, root diameter\u003c/p\u003e\n\u003cp\u003e3.6.2 Co-localization analysis of SCV and SNP in association panel\u003c/p\u003e\n\u003cp\u003eA total of fourteen MTAs were co-located via SNP and SCV marker association analysis (Table S5). Under NCE conditions, eight MTAs were detected with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging 4.64% to 7.74% for SNP-MTAs and 4.62% to 6.04% for SCV-MTAs. Under SCE conditions, a total of six MTAs were co-located via SNPs and SCVs, with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ranging 6.59% to 14.49% for SNP-MTAs and 5.68% to 6.82% for SCV-MTAs. Among these MTAs, Mr5A-6 and \u003cem\u003eQRd.sxau-5A\u003c/em\u003e (\u003cem\u003e5A _ 502688179\u003c/em\u003e) were significantly associated with RD, explaining the highest \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of root traits (Fig.3, Table.S5). \u003cem\u003e6A_597623440\u003c/em\u003e (Mg6A-9) had the same physical location as the peak markers of \u003cem\u003eQSdw.sxau.6A, QRdw.sxau.6A\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;QDw.sxau.6A\u0026nbsp;\u003c/em\u003e(Fig.3). These loci were stable in multiple environments, indicating that \u003cem\u003e6A_597623440\u003c/em\u003e was a stable locus associated with the seedling root traits, and suggesting that related genes regulating root architecture may exist within this loci interval on chromosome 6A.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe locus \u003cem\u003eQDw.sxau-6A\u003c/em\u003e was stably detected in both NCE and SCE, explaining 5.56%-13.60% of the phenotypic variation. Mg6A-9 was significantly and stably associated with DW in SCE, explaining 6.03-6.35 % of the phenotypic variation. According to the Zheng et al. (2022) analysis of LD attenuation distance,\u003cem\u003e\u0026nbsp;QDw.sxau-6A\u0026nbsp;\u003c/em\u003eand Mg6A-9 were on the same locus (Fig.4a-c). In the same genetic interval, Mr6A-11 and Mg6A-10 also were significantly associated with root traits, indicating that this interval on chromosome 6A is an important region for regulating root structure. The locus for the peak SNP marker \u003cem\u003e6A_597623440\u0026nbsp;\u003c/em\u003ein \u003cem\u003eQDw.sxau-6A\u003c/em\u003e had positive effects on root traits. The favored allele for this locus had significant effects on the root traits RDW, MRL, RS, TRL, RV, and RN. Varieties with favorable alleles showed increases in RDW, MRL, RS, TRL, RV, and RN (Fig.4d), indicating that \u003cem\u003eQDw.sxau-6A\u0026nbsp;\u003c/em\u003ehad significant effects on root traits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.4.\u003c/strong\u003e Analysis of \u003cem\u003eQDW.saw-6A\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;a\u003c/strong\u003e Idiogram of Mg6A-9 identified by Oligo-FISH.\u003cstrong\u003e\u0026nbsp;b\u003c/strong\u003e Physical map for \u003cem\u003eQDW.saw-6A\u003c/em\u003e. \u003cstrong\u003ec\u003c/strong\u003e Manhattan plot of \u003cem\u003eQDW.saw-6A\u003c/em\u003e identified by GWAS. \u003cstrong\u003ed\u003c/strong\u003e Effect of \u003cem\u003eQDW.saw-6A\u003c/em\u003e on root related traits.\u003c/p\u003e\n\u003cp\u003eRDW, root dry weight; MRL, maximum root length; RS, root surface-area; TRL, total root length; RV, root volume; RN, root number\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.6.3\u0026nbsp;Co-localization analysis of association panel and DH population\u003c/p\u003e\n\u003cp\u003eTo more precisely locate loci related to root traits, the co-localization results of the association panel and the DH population were analyzed. Among the QTL obtained by linkage analysis, 10 QTL were also identified via association analysis (Table S6). Five QTL were stably detected in multiple datasets of the DH population, explaining 4.59 % to 17.51 % of the phenotypic variation. \u003cem\u003eQRd.sxau-4D\u0026nbsp;\u003c/em\u003ewas detected in all datasets of the DH population with the highest \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e value. This locus has been reported as significantly correlated with root-shoot ratio\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Yang et al. 2021a). The physical location of \u003cem\u003eQMrl.sxau-3A\u003c/em\u003e in the DH population was 0.3 Mb from the locus Mr3A-4 related to the TRL in the association panel, which explained \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e of 5.11 % (Table S6). In addition, \u003cem\u003eQDw.sxau-1D\u003c/em\u003e was stably detected by multiple datasets in both the association panel and the DH population (Fig.5a-b). \u003cem\u003eQDw.sxau-1D\u003c/em\u003e explained 5.17-5.92 % of the phenotypic variation for DW in the DH population and 7.24-16.34 % in the association panel. This locus also has been reported to be significantly related to the root diameter (Yang et al. 2021a). The favorable alleles of \u003cem\u003eQDw.sxau-1D\u003c/em\u003e had significant effects on root traits RDW, RFW, DW and RV (Fig.5c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.5.\u0026nbsp;\u003c/strong\u003eAnalysis of \u003cem\u003eQDW.saw-1D\u003c/em\u003e.\u003cstrong\u003e\u0026nbsp;a\u003c/strong\u003e Manhattan plot of DW with markers located on chromosome 1D. \u003cstrong\u003eb\u0026nbsp;\u003c/strong\u003ePhysical map for \u003cem\u003eQDW.saw-1D\u003c/em\u003e. \u003cstrong\u003ec\u0026nbsp;\u003c/strong\u003eGenetic linkage map for \u003cem\u003eQDW.saw-1D\u003c/em\u003e. d Effect of \u003cem\u003eQDW.saw-1D\u003c/em\u003e on root-related traits.\u003c/p\u003e\n\u003cp\u003eRDW, root dry weight; DW, plant dry weight; RFW, root fresh weight; RV, root volume\u003c/p\u003e\n\u003cp\u003e3.7. Effects of loci in association panel\u003c/p\u003e\n\u003cp\u003eThe effect of loci on root traits was analyzed in the association panel according to the type of allelic variation. For phenotypic traits and SNPs, variation at the nine loci differed in their effects on root traits (Fig.S5). Of these, \u003cem\u003e1B _ 110637082\u003c/em\u003e,\u003cem\u003e\u0026nbsp;2A _ 327206091\u0026nbsp;\u003c/em\u003eand \u003cem\u003e6A _ 597623440\u003c/em\u003e had the strongest effects (Fig.S5). The effects of \u003cem\u003e1A_592348186\u003c/em\u003e, \u003cem\u003e2D_584522338\u003c/em\u003e, \u003cem\u003e5D_450204045\u0026nbsp;\u003c/em\u003eand \u003cem\u003e5D_359103858\u003c/em\u003e were smaller. Among these loci, the proportions of favored alleles for loci \u003cem\u003e1A_592348186\u003c/em\u003e were more than 90%, whereas the proportion for loci \u003cem\u003e5D_450204045\u0026nbsp;\u003c/em\u003eand \u003cem\u003e5D_359103858\u003c/em\u003e were less than 10%. The association analysis between phenotypic traits and SCV showed that 11 loci varied significantly in their effects on root traits (Fig.S6). Mr1D-5, Mr3A-3, and Mr3A-4 significantly affected more than nine root traits, while Mg1D-2 had \u0026nbsp;lesser effect. The proportions of favorable alleles for loci Mr1A-1 and Mr2B-12 were more than 80%, whereas Mg1D-2, Mr3A-3, and Mr3A-4 were less than 10%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMolecular marker-assisted selection requires markers with strong effects. At present, only a single type of marker such as SNP or SSR are used to study additive effects, and there is no report describing the combined effects of cytological markers and SNP markers. In the present study, the additive effects on root traits of loci were analyzed by combining SNPs and SCVs. Markers with significant effects on all root traits were selected for analysis and additive effects were analyzed based on peak markers. Four SNPs and five SCVs had additive effects on root traits, including \u003cem\u003e1B_110637082\u003c/em\u003e,\u003cem\u003e\u0026nbsp;2A_327206091\u003c/em\u003e,\u003cem\u003e\u0026nbsp;5D_359103858\u003c/em\u003e,\u003cem\u003e\u0026nbsp;6A_597623440\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eMr5B-11, Mr1D-5, Mr2B-12, Mg2B-6 and Mr3A-4. The average values of root traits increased as the number of positive alleles increased (Fig. 6). This result confirms the usefulness of these markers for selecting wheat cultivars for improved root systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.6.\u003c/strong\u003e Effects of increasing numbers of favorable alleles for stable loci on root traits using SNPs and SCV in the association panel\u003c/p\u003e\n\u003cp\u003eRFW, root fresh weight; RS, root surface-area; RV, root volume; TRL, total root length\u003c/p\u003e\n\u003cp\u003e3.8. Identification of candidate genes for the SNP and SCV co-localized locus \u003cem\u003eQDw.sxau-6A\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCombined analyses of the two types of markers indicated that some of the overlapping segments were close to candidate genes. Overall, 439 high-confidence genes from the functional interval were annotated. Gene annotation and orthologous gene analysis indicate that \u003cem\u003eTraesCS6A02G372300\u003c/em\u003e, \u003cem\u003eTraesCS6A02G382900\u0026nbsp;\u003c/em\u003eand \u003cem\u003eTraesCS6A02G365100\u003c/em\u003e are likely involved in either root development or lateral root formation.\u003cem\u003e\u0026nbsp;\u003c/em\u003eGene annotation for \u003cem\u003eTraesCS6A02G372300\u003c/em\u003e indicated an association with lateral root development and maintenance of root viability, with homologous genes \u003cem\u003eRID2\u003c/em\u003e (\u003cem\u003eAT5G57280\u003c/em\u003e) (Konishi et al. 2003) in Arabidopsis and \u003cem\u003eOs02g0804300\u003c/em\u003e in rice. \u003cem\u003eTraesCS6A02G382900\u003c/em\u003e was associated with regulation of root development and nutrient reservoir activity, and it was homologous to \u003cem\u003eOs01g0284500\u003c/em\u003e in rice. The function of \u003cem\u003eTraesCS6A02G365100\u003c/em\u003e was annotated as related to lateral root formation and growth regulation. It was homologous to \u003cem\u003eRLF\u003c/em\u003e (\u003cem\u003eAT5G09680\u003c/em\u003e ) in Arabidopsis and \u003cem\u003eOs07g0232200\u003c/em\u003e in rice. \u003cem\u003eRLF\u003c/em\u003e has been shown to control early cell division in the initiation of lateral roots formation (Ikeyama et al. 2010). The expression levels of genes within the functional interval were analyzed. There was high variation between the expression patterns in different tissues. \u003cem\u003eTraesCS6A02G414300\u003c/em\u003e (G35) and \u003cem\u003eTraesCS6A02G399500\u003c/em\u003e (G15) were specifically expressed only in roots, and \u003cem\u003eTraesCS6A02G356900\u003c/em\u003e (G33) was specifically expressed in embryonic and adventitious roots. \u003cem\u003eTraesCS6A02G382900\u003c/em\u003e (G6) and\u003cem\u003e\u0026nbsp;TraesCS6A02G403000\u003c/em\u003e (G40) showed high expression levels in root, suggesting that both are candidate genes for \u003cem\u003eQDw.sxau-6A\u003c/em\u003e (Fig.7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig.7. a\u003c/strong\u003e The Manhattan plot for\u003cem\u003e\u0026nbsp;QDw.sxa\u003c/em\u003eu-6A; \u003cstrong\u003eb\u003c/strong\u003e Linkage disequilibrium heatmap of candidate region on chromosome 6A; \u003cstrong\u003ec\u003c/strong\u003e Transcriptome data for candidate region genes in different tissues. A, radicle; B, roots; C, axillary roots; D,shoots; E, coleoptile; F, stem; G, flag leaf; H, leaf; I, pistil; J, stamen; K, spike; L, spikelets; M, grain; N, endosperm; O, aleurone layer\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e4.1. Soil culture condition at the seedling stage accurately reflects the root structure at the adult stage\u003c/p\u003e\n\u003cp\u003eRoot systems are the most important part of absorbing water and nutrients\u0026nbsp;from the soil, and the differences in root architecture directly affects absorption efficiency (Ober et al. 2021). Root architecture is made up of traits with different functions, such as root length, number of roots, root surface area and root biomass.\u0026nbsp;Wheat root-related traits are complex quantitative traits. Genetic improvement of wheat has mainly focused on agronomic and yield-related traits of aboveground parts (Guo et al. 2023; Bennani et al. 2022; Liu et al. 2017; Bennett et al.2012). By contrast, the knowledge of morphology and genetic regulation of root systems in wheat is lacking. However, root architecture directly affects the morphology and yield of the above-ground plant parts. Thus, strengthening the identification of root characteristics and analyzing the genetic basis of root traits will aid breeding for improved root systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe difficulties in measurement of root-related traits in the field have hindered genetic dissection.\u0026nbsp;Consequently, measuring roots at the seedling stage under controlled conditions has been used to represent the morphology and distribution of the roots at the adult stage. There are many methods for investigating root traits at the seedling stage, such as hydroponics with nutrient solution (Ayalew et al. 2017), gel chambers (Christopher et al. 2013), a paper culture (Bai et al. 2013), sand culture (Hamada et al. 2012) and soil culture (Cao et al. 2014). Seedling stage measurements are small experimental error, simpleoperation, high throughput, and low sampling damage. Combined with software for digital image analysis, the root-related traits of wheat can be measured rapidly. Xie et al. (2017)\u0026nbsp;assessed root depth, root width, and TRL by using a germination\u0026nbsp;paper-based \u0026lsquo;pouch and wick\u0026rsquo; phenotyping system and found that more seminal roots and longer TRL at the seedling stage were crucial for early root growth and high yield at the adult stage.\u0026nbsp;With a soil culture method Zhao et al (2021) identified 13 indicators, such as RS, root volume and root biomass, and found that the maximum root length and root biomass could be used for excellent germplasm selection. As verified in the present study, Liu et al (2019) found a significant correlation between seedling root traits under hydroponic and soil culture conditions.\u0026nbsp;However, due to differences in experimental methods and germplasm, previous studies did not analyze the differences between hydroponic and soil cultures. In the present study, the root traits of 282 wheats were compared under both NCE and SCE and significant differences between the two culture methods were found. The root traits of seedlings grown under SCE conditions significant correlated with certain agronomic traits of adult plants grown under field conditions. Correlation coefficients between SDW, DW, SFW, FW and most agronomic traits were significant in SCE, but non-significant in NCE. These results showed that accurate phenotypes could be obtained from SCE-grown seedlings.\u0026nbsp;Because SCE and NCE measurements were correlated for some traits, the seedling NCE measurements of RFW, RDW, RS and RV could be used to replace the more laborious SCE.\u003c/p\u003e\n\u003cp\u003e4.2.\u0026nbsp;SCV provides novel insight for locating genes related to root traits in wheat\u003c/p\u003e\n\u003cp\u003eRoot-related traits are regulated at the molecular, pathway, network, and cellular levels. At present, QTL and genes related to roots have been reported at the nucleic acid level.\u0026nbsp;For example, Alemu et al (2021) evaluated traits such as total root length, root number and root biomass with 192\u0026nbsp;Ethiopian durum wheats and identified 18 loci affecting the absorption of water and nutrients by the root system. Using a DH population, thirty-four QTL related to root architecture and biomass were identified, among which \u003cem\u003eQRRS.caas-4DS\u003c/em\u003e had the highest \u003cem\u003eR\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eof 20.41%, which was crucial for improving nutrient utilization efficiency of the root system (Yang et al. 2021b). The RIL population was used to evaluate five root traits such as total root length, root surface area and root volume, and a total of four root-related QTL were identified\u0026nbsp;(Yang et al. 2017). Some genes related to roots have been reported.\u0026nbsp;Wang\u0026nbsp;et al. (2023b) found that\u0026nbsp;\u003cem\u003eTabHLH123\u003c/em\u003e regulated the expression of genes controlling crown-root development and auxin transport. Hu et al. (2018) identified wheat \u003cem\u003eTaWRKY51\u003c/em\u003e as a crucial factor that functions in lateral root formation by regulating ethylene biosynthesis. Zhuang et al. (2021) cloned a novel ERF transcription factor gene\u003cem\u003e\u0026nbsp;TaSRL1\u0026nbsp;\u003c/em\u003efrom wheat which is mainly expressed in root and is associated with root traits. In summary, some progress has been made in identifying genetic effects of root-related QTL and genes at the nucleic acid level. However, there are few studies on root development and regulatory mechanisms at the chromosome level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe variation of chromosome structure plays an important role in environmental adaptability, stress resistance, and the formation of desirable traits in wheat (Zhang et al. 2023;\u0026nbsp;An\u0026nbsp;et al. 2019). Thus, the identification of chromosomal structural variants can provide new insights for the genetic characterization of root traits.\u0026nbsp;In wheat, the genetic effects of SCVs have been studied rarely, and only SCVs associated to agronomic traits have been reported (Zhao et al. 2022; Zhao et al. 2023).\u0026nbsp;In the present study is the first to study SCVs related to root traits at the seedling stage. In the association panel, 14 SCVs significantly associated with root traits were detected. Mr1B-3, Mr3A-3, and Mr3A-4 were stably detected by multiple datasets under both SCE and NCE culture conditions, explaining 4.74% - 9.07% of the phenotypic variance. Among these, Mr1B-3 was significantly associated with many root traits such as root biomass, root surface area, root volume, and total root length. Compared to germplasm without favorable alleles, accessions with favorable alleles for locus Mr1B-3\u003csub\u003eCC\u003c/sub\u003e showed greater RDW, RFW and RV (Fig.S6). Co-localization analysis showed that \u003cem\u003eQDw.sxau-6A\u003c/em\u003e (association analysis between root traits and SNPs) and Mg6A-9 (association analysis between root traits and SCVs) were located in the same physical position on chromosome 6A. The genetic contribution of locus\u0026nbsp;\u003cem\u003eQDw.sxau-6A\u003c/em\u003e (Mg6A-9) for root traits was high (Fig.4d) and candidate genes for this locus are probably related to the regulation of root structure.\u003c/p\u003e\n\u003cp\u003eBreeders always select alleles associated with superior phenotypes (Wu et al. 2022), including adaptation of root structure to the local environment. The requirements of root structure change will vary depending on specific wheat breeding goals.\u0026nbsp;In the present study, the allelic variants for loci peak markers were associated with effects on root structure, such as \u003cem\u003e1B_110637082\u003c/em\u003e, \u003cem\u003e2A_327206091\u003c/em\u003e, Mr5B-11, and Mr2B-12. These markers\u0026nbsp;have potential for practical use in different wheat breeding regions. In rainfed dryland areas, which tend to select deep-rooted varieties, Hap1-type markers can be used. For example, Mr3A-4-Hap1-A increased the maximum root length and total root length, and Mr1D-5-Hap1-A increased root biomass and root number, and Mr5B-11-Hap1-C increased root biomass and root surface area.\u0026nbsp;In addition, these markers have additive effects. Therefore, combined marker Hap1 types could be used to select deep-rooted and large-rooted varieties in arid and semi-arid areas. In contrast, shallow-rooted varieties improve overall yield per unit area\u0026nbsp;in regions with multiple irrigations by increased density of planting and population advantages. Markers for decreasing root mass and size (Hap2-type) could be used for selection in areas with multiple irrigation,\u0026nbsp;including markers for Mr3A-4-Hap2-C, Mr1D-5-Hap2-C and Mr5B-11-Hap2-G. As combinations of these alleles usually show additive effects for root improvement, selection using multiple markers will be more effective than selection using single markers. Hence, pyramiding markers for favorable allelic should benefit future breeding for root improvement. The present study provides different types of molecular markers for root improvement and provide a new direction for the integration and use of multiple markers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.3. Association analysis combined with linkage analysis to identify loci\u003c/p\u003e\n\u003cp\u003eSeedling root traits are inherited together, and it is difficult to independently select for individual traits. Traditional analysis of complex traits usually uses linkage analysis in biparental populations to estimate the effect of the target trait (Zandipour et al. 2020).\u0026nbsp;However, linkage analysis only detected relatively favorable allelic variations in both parents. GWAS based on linkage disequilibrium does not construct large mapping populations to detect multiple alleles at the same position. In recent years, combining association analysis with linkage mapping\u0026nbsp;has been used to yield more credible results. In a recent study concerning yield‑related traits and drought tolerance, GWAS and QTL mapping were used to identify genomic regions associated with five 5 agronomic traits (Guo\u0026nbsp;et al. 2023). Twelve loci controlling kernel number per spike, explaining up to 25.53% of the phenotypic variation, were found with GWAS and linkage analysis (Shi et al. 2017).\u0026nbsp;However, genetic studies of wheat root traits have mostly used a single method, such work by as Li et al (2020), Yang et al (2021a), Ma et al (2022), and Liu et al (2019), among others.\u0026nbsp;In the present study, association mapping and linkage analysis revealed genomic regions for all 12 root-related traits at the seedling stage. A total of 10 QTL\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere detected not only with GWAS but also with linkage mapping. Among these QTL, \u003cem\u003eQDw.sxau-1D\u003c/em\u003e was stably detected by multiple datasets in both populations,\u0026nbsp;and had a significant impact on RDW, RFW, DW, FW, and RV. Thus, this is a key QTL for root traits.\u003c/p\u003e\n\u003cp\u003eYang et al. (2021b) reported the marker\u003cem\u003e\u0026nbsp;AX-109966788\u0026nbsp;\u003c/em\u003efor root length on chromosome 7A using a DH population generated from a cross of Yangmai 16 and Zhongmai 895.\u0026nbsp;This locus coincides with the physical location of \u003cem\u003eQMrl.sxau-7A\u0026nbsp;\u003c/em\u003ein the present study, indicating that two loci likely represent the same locus. Meanwhile, we identified three QTL-rich clusters associated with wheat root traits. \u003cem\u003eQRd.sxau-1A\u003c/em\u003e was located on chromosome 1A (Table 2). This region has QTL-rich clusters for wheat root-related traits, indicating this interval may be related to wheat root architecture. \u003cem\u003eQRdw.sau-1A\u003c/em\u003e was associated with root dry weight (Li et al. 2020) and was co-located with \u003cem\u003ewsnp_Ex_c1861_3507281\u003c/em\u003e for root fresh weight (Salarpour et al. 2020), and with \u003cem\u003eQRL.caas-1AL\u003c/em\u003e (C) for root length under normal nitrogen conditions (Yang et al.2021a). \u003cem\u003eQRdw.sxau-6A\u003c/em\u003e was located within 596.50 Mb - 602.16Mb on chromosome 6A (Table 3). Comparative analysis revealed that this locus overlaps with three loci for root length, \u003cem\u003eQRL.caas-6AL\u003c/em\u003e (Yang et al.2021b), \u003cem\u003eXgwm169\u003c/em\u003e (Ayalew et al.2017), and \u003cem\u003eQMrl.sau-6A\u0026nbsp;\u003c/em\u003e(Li et al. 2020), suggesting this region may be a QTL rich cluster for wheat root-related traits.\u003cem\u003e\u0026nbsp;\u003c/em\u003eA similar cluster was discovered via comparative analysis with locus \u003cem\u003eQDw.sxau-6B\u003c/em\u003e co-located with \u003cem\u003eQRL.caas-6BL\u003c/em\u003e for root length (Yang et al.2021b) and with \u003cem\u003eQRTN.caas-6BL\u0026nbsp;\u003c/em\u003efor root tip number (Yang et al.2021b). Presumably, this is due to pleiotropic effects. Research on the regulatory mechanisms of SCVs is insufficient. Therefore, next steps include analysis of candidate genes related to SCVs and their mechanism of regulation of root traits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e JZ and JLW designed the study. NCW and JBT performed the experiments. NCW, JJZ, BBW, LQ, XHL, and XWZ analyzed experimental results. NCW and YQH wrote the manuscript. JZ, JLW, and JJZ helped revise the manuscript. All authors read and commented on the final version for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This project was supported by National Key Research and Development Program of China (2023YFD1201003), The Key Research and Development Project of Shanxi Province (202102140601001-1), and Graduate Education Reform and Quality Improvement Program of College of Agriculture, Shanxi Agricultural University (2023YCX01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eAll data generated or analyzed in this study are available in the manuscript and Supplementary Information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e The authors declare that they have no conflicts of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlemu A, Feyissa T, Maccaferri M, Sciara G, Tuberosa R, Ammar K, Badebo A, Acevedo M, Letta T, Abeyo B (2021) Genome-wide association analysis unveils novel QTLs for seminal root system architecture traits in Ethiopian durum wheat. 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J Exp Bot 72(20):6977-6989. https://doi.org/ 10.1093/jxb/erab357\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4142603/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4142603/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The root system of wheat affects water and fertilizer use efficiency, stress tolerance, and agronomic traits. Using association analysis and linkage mapping, QTL associated with 12 seedling-stage root traits were identified with SNPs and SCVs under both hydroponic nutrient solution culture experiment (NCE) and soil culture experiment (SCE). Except for MRL, the root traits of seedlings under NCE and SCE differed significantly. Several seedling indicators, including RFW, RDW and RS, were significantly correlated with adult plant agronomic traits. Identification of RFW, RDW, RS, and RV by NCE is equivalent to SCE for subsequent research. Under NCE, 29 stable loci and 9 SCVs of 12 root traits were detected respectively by SNPs and SCVs association analysis. Under SCE, association analysis detected 23 stable loci with SNPs and 26 loci with SCVs. In the DH population, 21 stable QTL were detected by SNPs linkage analysis, and 6 SCVs were found by SCVs analysis. Co-localization analysis revealed that NCE and SCE simultaneously detected QRdw.sxau-6A, QRd.sxau-1B.2, and QDw.sxau-6A (5.56%-8.76% of R2). Mr1B-3, Mr3A-3 and Mr3A-4 were detected in both NCE and SCE (4.74%-9.07% of R2). In the association panel, SNPs and SCVs co-localized to 14 MTAs, of which Mr5A-6 and QRd.sxau-5A were significantly associated with RD. The association panel and DH population co-located 10 QTL, of which QDw.sxau-1D was stably detected. QDw.sxau-6A and Mg6A-9 overlapped in same genomic location containing candidate genes TraesCS6A02G372300, TraesCS6A02G382900 and TraesCS6A02G365100. The present study contributes novel insights into the genetics of root architecture in wheat.","manuscriptTitle":"Genetic dissection for seedling root-related traits using multiple-methods in bread wheat (Triticum aestivum L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 17:17:38","doi":"10.21203/rs.3.rs-4142603/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-04-07T06:12:54+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-05T12:15:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-23T05:48:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-03-21T06:06:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e7e0cde0-c69b-4aec-877f-57a15c60fc35","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T19:58:08+00:00","versionOfRecord":{"articleIdentity":"rs-4142603","link":"https://doi.org/10.1007/s00122-025-04847-6","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2025-03-07 15:57:41","publishedOnDateReadable":"March 7th, 2025"},"versionCreatedAt":"2024-04-10 17:17:38","video":"","vorDoi":"10.1007/s00122-025-04847-6","vorDoiUrl":"https://doi.org/10.1007/s00122-025-04847-6","workflowStages":[]},"version":"v1","identity":"rs-4142603","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4142603","identity":"rs-4142603","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}